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v

Strategic Studies Institute

and

U.S. Army War College Press

THE RISE OF IWAR: 

IDENTITY, INFORMATION, 

AND THE INDIVIDUALIZATION 

OF MODERN WARFARE

Glenn J. Voelz

October 2015

The views expressed in this report are those of the author and 

do not necessarily reflect the official policy or position of the 

Department of the Army, the Department of Defense, or the U.S. 

Government. Authors of Strategic Studies Institute (SSI) and 

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offer new and sometimes controversial perspectives in the interest of furthering debate on key issues. This report is cleared for 

public release; distribution is unlimited.

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This publication is subject to Title 17, United States Code, 

Sections 101 and 105. It is in the public domain and may not be 

copyrighted.
vi

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be forwarded to: Director, Strategic Studies Institute and U.S. 

Army War College Press, U.S. Army War College, 47 Ashburn 

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The author would like to thank a number of individuals and 

organizations providing important assistance and feedback during the course of this research. Foremost are the faculty, students, 
vii

and staff of the Security Studies Program at the Massachusetts 

Institute of Technology (MIT) and MIT’s Lincoln Laboratory for 

their support and generosity during the author’s fellowship.

A number of organizations hosted the author for general discussions on identity, technology, and broader issues relating to 

national security strategy. These include the Defense Forensics 

and Biometrics Agency, the Defense Forensic Science Center, U.S. 

Special Operations Command, the Army Deputy Chief of Staff 

for Intelligence, the National Ground Intelligence Center, the National Media Exploitation Center, the Identity Intelligence Project 

of the Office of the Defense Intelligence Agency, and several offices within the Director of National Intelligence. The author also 

benefitted greatly from dozens of professional discussions and 

seminars presented during the 2014 Global Identity Summit.

In particular, the author would like to acknowledge the significant support of Ed Wack at MIT’s Lincoln Laboratory, Jim 

Shufelt of the U.S. Army War College, and Dr. Barry Posen and 

Dr. Owen Cote of MIT’s Security Studies Program. Their support 

and critique of the ideas developed in this monograph have been 

invaluable. Any errors of fact or analysis are the author’s alone.

ISBN 1-58487-703-0

ix

FOREWORD

During a recent address to the National Defense 

University on U.S. counterterrorism strategy, President Barack Obama cautioned that “we must define 

the nature and scope of this struggle, or else it will define us.” His comments hinted at the dramatic transformations of the U.S. military and national security 

apparatus since September 11, 2001 (9/11). Notable 

among these have been a new operational emphasis 

on the threats posed by nonstate actors and individual combatants. This trend represents a major shift 

from the Cold War era paradigm focused primarily 

on conventional threats from state-based adversaries. 

This strategic reprioritization has evolved into new 

military doctrines focused on the task of defeating 

networks rather than formations and technical innovations designed for identifying, screening, and targeting individual combatants on the battlefield. This 

operational focus has also made the issue of identity 

central to U.S. national security strategy—whether 

screening individual threats at the borders, segregating them on the battlefield, or targeting them across 

the spaces in between.

In this monograph, Colonel Glenn Voelz examines 

this defining feature of recent conflicts, specifically the 

doctrinal and technical innovations giving rise to this 

new operational paradigm. He describes the central 

pillars of individualized warfare, including the rise of 

identity-based targeting and the key role of information technology in conducting these operations. This 

work contributes to an important dialogue concerning 

lessons learned from a decade of global counterterrorism operations and two extended counterinsurgency 

campaigns. It provides a useful case study on wartime 
military innovation by considering the policies and 

strategies that evolved in response to a new and unexpected adversary. He concludes this monograph with 

an in-depth discussion covering a range of emerging 

technologies likely to define how this kind of war will 

be waged in the future.

 

DOUGLAS C. LOVELACE, JR.

 Director

 Strategic Studies Institute and

 U.S. Army War College Press

x
xi

ABOUT THE AUTHOR

GLENN J. VOELZ is an Army intelligence officer and 

most recently the U.S. Army War College fellow at the 

Massachusetts Institute of Technology (MIT) Security 

Studies Program and MIT’s Lincoln Laboratory. He 

currently serves in the Intelligence Division of the International Military Staff at the North Atlantic Treaty 

Organization headquarters in Brussels, Belgium. During his career, Colonel Voelz has served in various 

positions at the Defense Intelligence Agency, on the 

Joint Chiefs of Staff at the Pentagon, and as the Senior 

Duty Officer in the White House Situation Room. His 

military duties have included assignments in Asia, 

Africa, the Middle East, and Europe. He previously 

served as an Assistant Professor of History at West 

Point. Early in his career, he was a member of the 

Army’s World Class Athlete Program and was a twotime U.S. Olympic Trials qualifier for Swimming in 

1988 and in Modern Pentathlon in 1996. Colonel Voelz 

is the author of several books and journal articles on 

a range of topics including diplomatic history, government contracting, intelligence policy, and military 

strategy. Colonel Voelz is a graduate of West Point 

where he was commissioned as an Infantry officer in 

1992. He holds advanced degrees from the University 

of Virginia and the National Intelligence University in 

Washington, DC. 

xiii

SUMMARY

During a decade of global counterterrorism operations and two extended counterinsurgency campaigns, 

the United States was confronted with a new kind of 

adversary. Without uniforms, flags, and formations, 

the task of identifying and targeting these combatants represented an unprecedented operational challenge for which Cold War era doctrinal methods were 

largely unsuited. This dilemma became the catalyst 

for a decade of doctrinal, technical, and organizational 

change premised on the central idea that nonstate actors and individual combatants were a salient national 

security concern and, therefore, legitimate military 

targets. This strategic reprioritization evolved into 

a new model of state warfare centered on the operational tasks of identifying, screening, and targeting 

individual combatants and defeating their networks.

This mode of warfare has been characterized by 

analytical methods focused on the systematic disaggregation of threats down to the lowest possible 

level—often the individual combatant on the battlefield. When irregular adversaries could no longer be 

differentiated by uniform or status, identity attributes 

became the new technical signature of battlefield targeting. Biographic, biometric, and forensics data became a critical component of the targeting process. 

The collection and analysis of this data required new 

information management technologies designed to reduce anonymity on the battlefield, penetrate complex 

networks, and differentiate friend from foe. This also 

required architectures able to process and communicate identity data across the entire national security 

apparatus.
xiv

This monograph examines the doctrinal, technical, 

and bureaucratic innovations that evolved in response 

to these new operational challenges. It examines the 

transition from a conventionally focused, Cold Warera targeting process to one optimized for combating 

networks and conducting identity-based targeting. 

It analyzes the policy decisions and strategic choices 

that were the catalysts of this change and concludes 

with an in-depth examination of emerging technologies that are likely to shape how this mode of warfare 

will be waged in the future.
1

THE RISE OF IWAR: 

IDENTITY, INFORMATION, AND 

THE INDIVIDUALIZATION 

OF MODERN WARFARE

Whenever you want to attack an army, besiege a city, 

or kill a person, first you must know the identities of 

their defending generals, their associates, their visitors, their gatekeepers, and their chamberlains . . .

 Sun Tzu, The Art of War1

INTRODUCTION

In late-2014, the United States reached the milestone of the 500th nonbattlefield targeted strike, 

operations that have killed some 3,600 people over 

the last decade.2 Beyond the numbers, this event is 

notable as one example of a new mode of state warfare based on military power being applied directly 

against individual combatants rather than formations. 

These so-called “targeted killings” are perhaps the 

most vivid example of the individualization of American warfare. The commander in chief now routinely 

reviews and approves strikes against named combatants, a phenomenon “without precedent in presidential history.”3

 However, this trend is not limited 

only to high-level counterterrorism efforts. It reflects 

a new strategic calculus that has elevated the status 

of the individual combatant into a foremost concern 

of national security policy and made the targeting of 

these entities a major driver of doctrinal and technical 

innovation on the battlefield.

The attacks of September 11, 2001 (9/11) and two 

extended counterinsurgency campaigns presented the 

United States with adversaries for which it was large-
2

ly unprepared. These new opponents did not fight as 

conventional formations or within a clearly defined 

battle space. Rather, they were organized as distributed networks and small cells, composed of individuals often indistinguishable from the surrounding 

population. Without uniforms and flags, the task of 

identifying and targeting these entities presented an 

unprecedented operational challenge for which traditional warfighting approaches were largely unsuited. 

In response, the U.S. national security apparatus embarked upon a decade of doctrinal, technical, and organizational innovation premised on the central idea 

that individual combatants represented a salient national security concern and legitimate military target. 

Within this new operational paradigm, the identification, screening, and targeting of “high-value individuals” and their associated networks became the focus of 

a new mode of state warfare—iWar.

THE PILLARS OF IWAR

The rise of iWar is a case study of military innovation centered on the operational task of identifying, 

screening, and targeting individual combatants and 

their networks; iWar is characterized by three distinct 

elements: Individualization, Identity, and Information. These pillars provide a conceptual framework 

for analyzing the dramatic changes in doctrine, technology, and strategic focus that have redefined how 

the United States wages war abroad and protects its 

borders at home.

• Individualization: Over the last decade, the 

U.S. national security apparatus shifted from 

its traditional focus on conventional military 

adversaries to an emphasis on nonstate threats 
3

and opponents fighting as dispersed, highly 

adaptive networks. This reorientation led to the 

adoption of new analytical methods and operational approaches based on the systematic 

disaggregation of threats down to the lowest 

possible level—often the individual combatant. In this mode of warfare, the targeting of 

“high-value individuals” became a paramount 

national security concern and key driver of 

doctrinal and technical innovation on the 

battlefield.

• Identity: As networked adversaries and individual combatants moved to the focal point of 

warfighting and domestic security concerns, 

there was a pressing need to identify and discriminate among these entities. In the age of 

iWar, the opponents were no longer generic 

soldiers who could be differentiated on the 

basis of status or uniform. As the targeting 

process became personalized, new kinds of information and methods were required which 

included biographic, biometric, and forensics 

data, and the use of network analysis for linking these identities to places, activities, and 

other actors. Identity attributes became the new 

technical signature of battlefield targeting and 

the first line of defense in the “watch listing” 

approach to homeland security.

• Information: Waging iWar depended upon a 

revolution of information management built 

around technologies designed for differentiating individual actors on the battlefield and segregating friend from foe. These tasks were unlike the analytical challenges of industrial age 

warfare and required new tools and methods 
4

for collecting, processing, and communicating identity information across the entire national security apparatus. The need to identify, 

screen, and target these threats at home and 

abroad made information management and 

data analysis the most important weapons in 

the age of iWar.

These pillars of iWar reflect a new operational 

paradigm that emerged in response to an unexpected 

adversary that fought as networks rather than formations. These combatants were not easily identifiable 

on the battlefield and used anonymity for operational 

advantage. Their activities were not limited to clearly 

defined battlefields or military targets. These characteristics enabled them to resist an overwhelming 

American advantage in conventional maneuver warfare, airpower, and logistics. This dilemma became the 

catalyst for a major reorientation of national security 

strategy based on the need to identify, screen, and target these individual combatants and their networks.

Within this new paradigm, operational progress 

could not be measured by the destruction of an adversary’s physical infrastructure or control of key terrain. 

This paradox led the United States toward a strategy 

of tactics, one based on the systematic disaggregation 

of threats down to the lowest possible level. This approach evolved into warfighting methods that turned 

“the fusion of operations and intelligence for the purpose of hunting high-value targets into a high art.”4

 In 

the waging of iWar, operational success has been defined by identifying these individual threats around 

the globe, segregating them on the battlefields, screening them at the borders, and targeting them across the 

spaces in between.
5

The tools and methods of iWar did not evolve as a 

result of grand overarching design. Rather, the pathway of innovation was defined by operational contingency, tactical adaptation, and new strategic priorities 

that emerged in response to an unexpected adversary. 

This led to a decade of significant doctrinal and technical innovation centered on the task of identifying and 

targeting threats from nonstate actors and individual 

combatants. This spurred an unprecedented revolution of information management and data sharing 

across the entire national security apparatus. It also 

fed major bureaucratic transformations that gradually eroded many of the traditional lines separating 

military operations, foreign intelligence activities, and 

domestic security functions. Together, these changes 

reflect a new strategic calculus that has placed threats 

from nonstate actors and individual combatants on 

equal footing with adversarial states as a driver of U.S. 

national security policy and military innovation.

RESEARCH FOCUS

This monograph is structured in two parts. The 

first section examines iWar as a case study of military 

innovation. It traces the course of doctrinal, technological, and organizational change within the U.S. national security apparatus in response to a new kind of 

adversary. It analyzes the dynamics of the post-9/11 

security environment, identifying specific policy decisions and strategic choices that became the catalysts 

of change and drivers of innovation. It examines how 

these changes evolved from the very specific operational challenge of identifying, screening, and targeting individual combatants on the battlefield and at the 

borders. Finally, it demonstrates how these changes 
6

challenged many of the underlying presumptions 

that have guided the conduct of state warfare in the 

modern age. 

The second part is more technically focused and 

speculative. It continues on the theme of military innovation by considering where current technical trends 

may lead as the United States continues to face threats 

from nonstate adversaries and individual actors. The 

monograph concludes with a scenario-based discussion that examines several emerging technology areas that may define how iWar is waged in the next 

generation.

THE CATALYSTS OF IWAR

The attacks of 9/11 largely debunked two enduring presumptions concerning the U.S. national security strategy of the post-Cold War era. The first was 

the traditional view that a combination of domestic 

social-cultural cohesion, stability among neighbors, 

and maritime separation could isolate the American 

homeland from the worst dangers emanating from 

failed states and transnational terrorism. A second 

presumption was that overwhelming strength in traditional war-fighting approaches based on maneuver 

warfare and firepower could deter the main threats to 

U.S. national security. However, the rise of al-Qaeda, 

two extended counterinsurgency campaigns, and persistent domestic security threats posed by nonstate actors served to challenge both of these presumptions.

In the aftermath of 9/11, the intelligence community, military, and homeland security entities faced a 

new kind of adversary that did not answer to a sovereign state, wear uniforms, or seek clearly defined 

geopolitical objectives. The nature of such threats had 
7

been articulated in various writings during the postCold War transition period, perhaps most prophetically in John Arquilla and David Ronfeldt’s Networks and 

Netwars. In this work, the authors described nonstate 

actors organized as decentralized hybrid structures, 

engaging in low-intensity conflicts by leveraging doctrines and technologies based on network design.5

Under the guise of “fourth generation warfare,” William Lind, T. X. Hammes, and others foresaw these 

networks and individual actors potentially supplanting the state as drivers of a new global order, an idea 

later sensationalized by Thomas Friedman’s thesis on 

“super empowered individuals.”6

All of these writers highlighted the fact that combating such adversaries would require that states rethink doctrines and technologies that were ill-suited 

for operations in a nonlinear battle space dominated 

by networks and information campaigns rather than 

formations and conventional maneuver. They described an emerging security environment defined by 

conflicts between state and nonstate actors, or states 

using nonstate actors as proxies.7 The common link 

among these predictions was the fact that these new 

adversaries would be structured as distributed networks rather than hierarchies. Conflicts would be 

sub-national or transnational in scope, and operations 

would tend to merge the strategic and tactical levels 

of war. Most importantly, these adversaries would be 

inherently difficult to identify and challenging to target. Defeating them would require new analytical approaches, organizational structures, technologies, and 

warfighting strategies. 

Many of the Netwar predictions have since been 

validated by U.S. experience in Iraq and Afghanistan, 

the ongoing campaign against global terrorism, and, 
8

more recently, in so-called “hybrid conflicts” characterized by decentralized and irregular warfighting 

waged by militias rather than professional armies. In 

all of these endeavors, the United States has struggled 

to contain and defeat adversaries operating under an 

organizational logic far different from the highly formalized and doctrinally based threats of the Cold War 

era. Instead, these new adversaries have been structurally complex and notably lacking obvious operational centers of gravity. These entities employ highly 

idiosyncratic tactics and adaptive strategies that have 

been difficult to analyze, template, and counter. Additionally, they have been particularly adept at exploiting commercial technologies, communications, and 

financial networks to expand influence. In some cases, 

they approach “state-like disruptive capacity” in their 

ability to execute attacks with global impact.

The demands of waging this new kind of war presented a far different challenge than that of Cold War 

era adversaries. Prior to 9/11, U.S. intelligence methods still primarily reflected a legacy “order of battle” 

paradigm focused on units, equipment, formations, 

and fixed doctrinal templates. Collection priorities 

were focused on long-term technical analysis of threat 

capabilities and the monitoring of strategic indications and warnings. However, the U.S. campaigns in 

Iraq and Afghanistan demanded an entirely new approach with greater emphasis on “human terrain” and 

network analysis. The population centric approaches 

of counterinsurgency required “not only the ability to 

positively identify individuals within the population, 

but also to understand social structure in terms of the 

social relationships among the population.”8 As a result, the intelligence community and military forces 

underwent a major transformation based on warfight-
9

ing theories that placed networks at the center of the 

analytical and operational challenge. This also meant 

that for the first time in modern American warfare, 

the issue of identity became a key data point and an 

operational “signature” that was critical for screening, 

segregating, and targeting individual combatants on 

the battlefield and stopping them at the borders.

iWar as National Security Strategy.

iWar was not born of specific design or as premeditated strategy. Instead, it evolved in piecemeal fashion as a result of ad hoc adaptations and incremental 

policy choices in the years following 9/11. The pillars 

of iWar have emerged at the center of the nation’s 

counterterrorism targeting methods, in the warfighting approaches adopted in Iraq and Afghanistan, and 

as the basis of a homeland security strategy built on 

the foundation of identity-based screening.

The Authorization for Use of Military Force 

(AUMF) provided an important initial catalyst for 

iWar in authorizing use of force against “nations, organizations, or persons,” thus setting the legal precedent for the targeting of individual combatants as 

an element of the nation’s broader counterterrorism 

strategy. This policy choice eventually manifested 

into a strategy of focused counterterrorism raids, 

so-called “targeted killings,” most notably by means 

of drone strikes in Pakistan, Yemen, and Somalia 

against top-tier leadership targets and key operational figures. This targeting methodology was gradually refined over time, particularly the shift from generic, signature-based targeting toward more highly 

focused “personality” strikes against specifically 

named individuals.
10

On the domestic front, the rise of iWar has been 

most evident in the emergence of the “watch listing” 

phenomena and identity-based screening programs 

that have become the central feature of post-9/11 

homeland security strategy. Over the last decade, the 

identities of millions of individuals have been added to 

such watch lists, augmented with detailed biographic 

information, biometric profiles, operational histories, 

and extended networks of associations and contacts. 

These databases have become the informational basis 

of an identity-based screening program designed to 

spotlight individual threats and screen against likely 

risks to transportation networks, critical infrastructure, and domestic security.

Beyond counterterrorism targeting and homeland 

defense, the iWar paradigm has perhaps been most 

evident in the evolution of military doctrine, technologies, and warfighting approaches used in Iraq 

and Afghanistan. The population-centric approaches 

and targeting methods that gradually evolved during 

these campaigns were deeply influenced by network 

theory and analytical methods emphasizing the role of 

low-level groups, key influencers, and individual actors as critical variables in establishing local security. 

This operational focus led to a decade of rapid military technology innovation that introduced a range 

of new tools to the battlefield specifically designed to 

support identity management and personality-based 

targeting, including drones, biometrics, forensics, and 

advanced data processing systems. This new paradigm represented a major reorientation of military 

focus away from conventional warfighting methods 

toward a new model of state warfare centered on the 

identification, screening, and targeting of individual 

combatants.
11

iWar as a New Model of State Warfare.

Beyond new technologies and doctrines, the rise of 

iWar represents a profound departure from the foundational presumptions of the Westphalian system that 

has defined the context of state warfare for over 300 

years since the end of the Thirty Years War. This historical moment marked an important transition point 

from the age of private mercenary conflicts toward 

a modern construct of warfare in which combatants 

came to be viewed as instruments of the state, acting 

on behalf of political sovereigns.9 This symbolized a 

“depersonalization” of conflict as soldiers assumed 

collective identity as members of professional armies. 

Jean-Jacques Rousseau’s seminal treatise on political 

power best articulated the significance of this transition, noting that modern warfare was no longer a: 

relationship between one man and another, but a relationship between one state and another, in which 

individuals are enemies only by accident, not as men, 

nor even as citizens, but as soldiers.10

This conceptualization provided the intellectual 

foundation for subsequent development of legal 

categorizations governing treatment of prisoners, 

wounded soldiers, civilians on the battlefield, and the 

basis for defining lawful combatency. Thus, under 

the Westphalian construct, soldiers became “generic” 

members of their national armies in terms of legal status and appearance. Uniforms emerged to distinguish 

soldiers from civilians and provide the operational 

context for lawful targeting and wartime protections.11

Within this mode of warfare, combatant privileges, 

obligations, and rules of engagement were no longer 
12

linked to individual identity but rather to a soldiers’ 

generic status as the member of a state formation.12

Over time, this evolved into a normative framework 

governing the conduct of state warfare and conventions of military targeting. 

Since 9/11, this construct has been directly challenged by a series of U.S. conflicts waged against 

networks rather than nations, with opposing forces 

comprised of “unprivileged enemy belligerents.”13

In this context the United States has conducted persistent battlefield operations against fighters who are 

legally disqualified from the privileges of combatant 

status as a result of joining or substantially supporting nonstate armed groups in the conduct of hostilities. This has created operational ambiguity where the 

traditional logic of status-based targeting no longer 

functions in practice. In its place, the U.S. military has 

evolved toward highly individualized approaches to 

threat assessment and identity-based targeting where 

adversaries are no longer “generic” fighters. 

This new operational paradigm reflects a personalization of warfare where the legitimate use of military 

force has become “tied to quasi-adjudicative judgments about the individual acts and roles of specific 

enemy figures.”14 Targeting is increasingly based on 

an individuated assessment of specific combatants, 

determined through evidentiary analysis and weighing operational relevance within a larger network 

apparatus. This represents a dramatic transformation in the targeting criteria applied on the modern 

battlefield. This has also changed how information is 

gathered, analyzed, and used in support of military 

operations, such as the emergence of “evidence-based 

targeting.”15
13

The rise of iWar has also challenged another tacit 

norm of the Westphalian construct, specifically the 

general prohibition against direct targeting of political leadership as a tactic of modern warfare. With 

the rise of professional militaries, political leaders no 

longer led armies directly into battle, thus creating a 

clear differentiation between those who conduct war 

at the policy level versus those who wage it on the 

battlefield.16 The practical implication has been that 

“leadership strikes” against political figures or nonoperational targets generally have not evolved as a central component of military strategy.17 However, this 

convention has been complicated by the ambiguity 

of distinguishing between operational and “political” 

leadership among nonstate groups. For instance, the 

United States has conducted lethal targeting against 

individuals variously described as religious leaders 

or spokesmen associated with extremist groups on 

the grounds that such individuals provide substantive support for terrorism activity. Yet, conventional 

interpretations of the law of armed conflict generally 

limit targeting to those directly participating in hostilities, while exempting individuals such as religious 

personnel or other civilians functioning in nonoperational roles. The iWar paradigm has complicated 

these distinctions, as status-based or functional criteria no longer offer a clear template for targeting these 

individual combatants.

There are also larger policy implications as individual combatants rather than conventional formations become the focus of operational targeting. The 

iWar paradigm has created an arbitrary zone of conflict with few discernable geopolitical, jurisdictional, 

or temporal boundaries, a fact tacitly acknowledged 

by successive U.S. administrations.18 As one observer 
14

recently noted, a war against a constantly changing 

set of actors, moving freely from place to place and 

from organization to organization, can have no clearly defined “enemy” and does not end with a peace 

treaty.19 Within such a conflict the categories of battlefield, combatant, and hostilities “no longer have clear 

or stable meaning.”20 This dilemma has been acutely 

demonstrated in the ongoing diplomatic and legal 

controversies surrounding nonbattlefield targeting 

and the issue of indefinite military detention.

Under the Westphalian construct, war termination 

occurs within a recognized framework, negotiated by 

terms of peace, and a normalized process for orderly 

demobilization and repatriation of combatants. However, campaigns in the age of iWar have not adhered 

to these traditional conventions. These conflicts have 

produced cohorts of wayfarer warriors, eager to apply 

their skills and experience across multiple theaters of 

conflict and acts of terrorism directed outside of recognized combat zones. This situation fundamentally has 

changed the operational significance of identity and 

necessitated strategies based on continuous monitoring of career fighters, both on and off the battlefield. 

Recidivism and a persistent global circulation of fighters have presented an entirely new security dilemma 

for nation-states, one that does not offer any clear 

path toward conventional military victory or orderly 

demobilization of combatants.

This situation reflects what some observers have 

called a hybridization of warfare as military operations increasingly span a broad spectrum of activities 

existing between the extremes of war and peace. As 

a mode of state conflict, iWar occupies this gray area 

between conventional warfare and law enforcement, 

sharing characteristics of both, yet without the pro-
15

cedural norms and clear legal context of either. For 

the United States, this has been characterized by the 

concurrent, and often integrated, use of traditional 

military measures such as kinetic strikes and battlefield detention, combined with law enforcement-like 

approaches based on evidentiary analysis, arrest, and 

prosecution. These methods evolved largely as a default solution for waging war across an unbounded 

battle space where “combatants” could be encountered on a battlefield as well as in an airport boarding 

queue. Indeed, some scholars have observed that this 

new mode of conflict, waged by state armies against 

individual combatants, has no existing legal framework upon which to base lawful targeting decisions.21

Indeed, the operational trend toward highly personalized, identity-based targeting has challenged many of 

the conventions that have informed the use of military 

force by state powers for generations.

In sum, the rise of iWar represents a new mode of 

state warfare where the threat of individual combatants and networks supplanted conventional formations at the focal point of national security strategy. 

The next section examines iWar as an example of 

doctrinal innovation and considers how approaches 

to warfighting evolved since 9/11 in response to this 

new kind of adversary.

iWar as a Driver of Doctrinal Innovation.

The pillars of the iWar paradigm are clearly visible through the last decade of doctrinal innovation, 

particularly with regard to U.S. approaches to counterterrorism and counterinsurgency strategies. While 

these missions represent a distinct set of objectives 

and methods, on a conceptual level they share an 
16

important commonality in placing networks and individual actors at the center of the analytical and operational challenge. Examination of this doctrinal progression is a useful exercise for understanding how 

these concepts evolved over time into the military’s 

institutional thinking. These doctrinal shifts provide 

important perspective into how organizational leaders understood the nature of post-9/11 adversaries 

and the methods needed to defeat them.

Among the early lessons of the campaigns in Iraq 

and Afghanistan was that “conventional warfare approaches often were ineffective when applied to operations other than major combat, forcing leaders to 

realign the ways and means of achieving effects.”22

The Army’s recent targeting manual notes how the 

operational focus of conventional warfare “is to find 

and destroy ships, tank formations, or infrastructure.” 

Conversely, the manual observes that, in counterinsurgency, the most difficult task is simply determining 

who is the enemy.23 Reflecting these new operational 

challenges, a 2007 report by the Defense Science Board 

observed that the task of distinguishing combatant 

identities had become an increasingly important concern on the battlefield, particularly as status-based 

signatures “diminish in incidence and usefulness.”24

These challenges led the U.S. military to undertake 

a decade of major doctrinal change focused on finding better methods for waging war against networks 

rather than formations, and targeting individual combatants rather than platforms. Even as U.S. forces embraced counterinsurgency theory with emphasis on 

governance and stability measures, much of the dayto-day operational focus in both Iraq and Afghanistan 

gravitated toward highly refined targeting efforts 

designed to “identify and separate the reconcilables 
17

from the irreconcilables.”25 A key component of establishing local security in these campaigns focused on 

aggressive efforts to identify and segregate key actors 

within insurgent networks, neutralize their networks, 

and conduct kill/capture operations against top-tier 

targets.

The “Find, Fix, Finish, Exploit, Analyze, and Disseminate” (F3EAD) targeting approach evolved as the 

preferred methodology for identifying and engaging 

these high-value individuals (HVI).26 U.S. forces in 

both Iraq and Afghanistan applied the F3EAD process 

with great success against insurgent networks and terrorist cells. In Iraq, these network-based targeting approaches were used to develop all-source intelligence 

and provide “situational awareness of the local environment, its social networks, key decision-makers, 

and their motivations.” This approach was perhaps 

most famously applied during the effort to track, target, and kill terrorist leader Abu Musab al-Zarqawi.27

In support of future operations, General Stanley 

McChrystal described a tightly integrated operational 

process based on: 

analysts who found the enemy, drone operators who 

fixed the target; combat teams who finished the target 

by capturing or killing him; specialists who exploited 

the intelligence the raid yielded, such as cell phones, 

maps, and detainees; and the intelligence analysts who 

turned this raw information into usable knowledge.28

In Afghanistan, between 2009 and 2011, similar 

approaches were applied in targeting insurgent networks, using methods that enabled a five-fold increase 

in focused raids designed to capture or kill individual, 

high-level insurgents.29 Beyond active combatants, 

these methods were also applied for nonkinetic target-
18

ing against drug producers and criminal networks as a 

means of undermining financial support to insurgent 

networks. Since then F3EAD has migrated into the 

military’s conventional targeting doctrine and become 

part of the military’s institutional training programs.30

While the F3EAD model was developed specifically as a process approach for personality-based targeting, it evolved within a larger conceptual framework based on Attack the Network (AtN) theory. AtN 

is a prime example of doctrinal evolution centered 

on micro-level disaggregation of the battlefield and 

network-based analysis in support of targeting. AtN 

emerged originally as an analytical approach for combating improvised explosive device (IED) networks 

and was designed for focused offensive operations 

against complex networks comprised of financiers, 

IED makers, trainers, and supporting infrastructure. 

AtN emphasized the use of specialized intelligence 

resources and analytical methods for identifying 

critical nodes and associational links within an adversary’s network.31 As part of this process, AtN offered a framework sub-categorizing individual actors 

within a tiered schema of target prioritization. For 

example, tier-one targets included top-level leadership, tier-two targets were intermediaries with links 

to facilitators and the population, and tier-three targets were primarily low-skilled foot soldiers and 

general threats among the population. Variations of 

this basic approach have been applied against a broad 

range of missions, such as tracking Joseph Koni and 

Lord’s Resistance Army elements in Uganda, analyzing the spread of Boko Haram influence in Nigeria, 

and understanding threat finance patterns of narcotics 

traffickers in Latin America.

The development of F3EAD and AtN methods reflected the larger theoretical evolution and integration 
19

of Social Network Analysis (SNA) into the military’s 

doctrinal cannon. The use of SNA for deconstructing 

the structure of complex networks predates the recent use by the U.S. military with scholarly research 

dating back to the 1960s. Notable among these early 

pioneers was Stanley Milgram’s work on the “smallworld” phenomenon and theories of structural disintermediation describing the dynamics of complex 

social networks.32 More recently, researchers such as 

Duncan Watts broadened the application of network 

theory by demonstrating its relevance to phenomena 

such as disease contagion, consumer behavior, and the 

dynamics of social influence.33 During the 1990s, SNA 

methods came into wider use for law enforcement 

and crime pattern analysis; however, they remained 

somewhat on the margins of military doctrinal thinking.34 Admiral Arthur Cebrowski was among the first 

military leaders to apply these concepts directly to 

warfighting strategy in his influential work, Network 

Centric Warfare. He proposed a network-based concept 

of warfare using distributed sensors and precision 

targeting; however, at the time, he did not conceive 

of such methods being used specifically in the context of counterinsurgency or targeting of individual 

combatants.

The attacks of 9/11 became a primary catalyst for 

bringing SNA methods fully into the mainstream professional discourse on military strategy and targeting. 

Early works, such as Marc Sageman’s Understanding 

Terror Networks, explicitly applied SNA as a framework for understanding a new kind of adversary. The 

2006 publication of the influential Army counterinsurgency manual reflected these influences and brought 

SNA methods fully into the doctrinal cannon.35 John 

Nagl one of the architects of this doctrine, observed 
20

that the inclusion of SNA methods played a critical 

role in driving: 

the Army’s intelligence system away from a focus 

on analysis of conventional enemy units toward a 

personality-based understanding of the networks of 

super-empowered individuals . . .36

The new counterinsurgency manual described 

SNA as “a powerful threat evaluation tool” and introduced a new vocabulary into the military’s operational 

vernacular, including concepts such as core-periphery 

structure, density, centrality, cohesion, clustering, and 

network visualization. These ideas were directly applied to the task of analyzing terrorist and insurgent 

networks, and identifying key actors and influencers 

on the battlefield.37 The salient idea of applying SNA 

to the targeting process was how the “capture of one 

highly connected insurgent” from within a dense 

insurgent network could help counterinsurgents 

systematically neutralize the larger organizational 

structure.38

Within the context of counterinsurgency and counterterrorism operations, SNA provided the conceptual 

framework for analyzing adversary networks, and 

identifying functional roles, organizational positions, 

and influential actors. At the tactical level, SNA theory 

supported the practical need for conducting “pattern 

of life” analysis and developing associational links 

and activities matrices. This also enabled detailed 

network visualization by identifying key personalities, habits, locations, movement routes, and financial 

transactions down to the level of the individual combatant. Such information formed the basis of detailed 

“target information folders,” a preferred method for 
21

systematically representing individual nodes in the 

adversary network.

The intelligence products developed from social 

network analysis supported HVI targeting with actionable information, including detailed physical 

descriptions of individual targets, biographic histories, familial relations, biometric data, cell phone 

numbers, and even car descriptions.39 These methods 

were widely credited as being central to the tactical 

successes achieved during the “surge” period in 2007 

when joint fusion cells applied these techniques “to 

locate, target and kill key individuals” within terrorist 

organizations, insurgent cells, and Shia militias.40 The 

population-centric approaches to counterinsurgency 

and focused counterterrorism efforts placed “identity 

operations” at the center of efforts “to positively identify, track, characterize, and disrupt threat actors.”41

SNA-based targeting against high-value individuals were also leveraged against facilitation networks 

(finance, recruitment, training, logistics, media, command, and control) and in support of nonkinetic activities such as leaflet drops, “most wanted” posters, 

text messaging campaigns, and tip hotlines, all used 

to create a “spotlight effect” against specific threat 

actors.42

Since the end of combat operations in Iraq and Afghanistan, social network theory has matured more 

fully into a foundational component of doctrinal 

thinking, evident in the most recent versions of the 

Army’s Operations Field Manual, the new doctrinal 

publication for Intelligence Analysis, the Joint Intelligence Preparation of the Operational Environment, 

and the Army’s field manual on Targeting, among 

other sources.43 SNA techniques have also become 

part of the military’s institutional training programs 

and remain widely discussed in professional litera-
22

ture relating to the conduct of irregular and hybrid 

warfare, counterterrorism, and stability operations.44

Over the last decade as SNA techniques became 

more integrated into the operational targeting process, 

the military services (primarily the Army and Marine 

Corps) developed more formalized approaches to 

“identity operations.” The Marine Corps has arguably 

been the most forward leaning in terms of institutionalizing these operational lessons into doctrine.45 This 

strategy focuses on identifying individuals and networks seeking to disrupt operations while providing 

tactical commanders with “near real-time information 

to establish the identity, affiliations, and authorizations of an individual, to scientifically link people, 

places, and events, deny anonymity and freedom of 

movement.”46 The Marine Corps has integrated these 

“identity-based strategies” across the six-phase jointcampaign planning construct, reflecting the need to 

collect, exploit, and analyze these signatures well in 

advance of operations in order to reduce threat anonymity. This concept was recently codified in U.S. 

Marine Corps Identity Operations (IdOps) Strategy 2020

that provided a holistic vision and approach for the 

development of IdOps across the range of military 

operations.47

The Department of Defense (DoD) has recently 

introduced a concept for Identity Intelligence (I2) 

into joint doctrine, further refining the methodology 

for personality-based targeting.48 I2 is not an intelligence process per se, but rather the tailored analytical 

products derived from the fusion of identity attributes 

(biologic, biographic, behavioral, and reputational 

information) in the operational planning process. 

I2 integrates several distinct technical functional areas, including Biometrics Enabled Intelligence (BEI), 
23

Forensics Enabled Intelligence, Document and Media 

Exploitation, and other all-source data for the purpose 

of “connecting individuals to other persons, places, 

events, or materials” and analyzing patterns of life.49

This new doctrine identifies a key role for I2 across a 

wide range of mission areas to positively identify and 

distinguish specific actors on the battlefield. These include missions such as focused raids, checkpoint and 

area security operations, border control and maritime 

interdiction, force protection, support to host-nation 

rule of law, and analytical tasks requiring detailed 

“human terrain” mapping.

In sum, the security challenges of the last decade 

have clearly highlighted the increasing relevance 

of identity on the modern battlefield. This has been 

demonstrated in the evolution of counterterrorism 

targeting, as well as during counterinsurgency campaigns against irregular forces able to blend in with 

local populations and use anonymity for operational 

advantage. This mode of warfighting represents a significant departure from the doctrines of industrial-age 

warfare with its emphasis on large-scale maneuver, 

firepower, and conventional force engagement. This 

change has been reflected in new doctrinal approaches emphasizing tools such as SNA and IdOps into the 

planning and targeting process. 

The new doctrines of iWar have been designed 

specifically to meet the challenge of combating networked adversaries and the analytical demands of a 

highly refined targeting process focused on the lowest common battlefield denominator—the individual 

combatant. As the next section highlights, these approaches would not have been possible without the 

concurrent development of several key technologies 

that have enabled U.S. forces to collect and analyze 
24

large amounts of identity information for screening 

and targeting individual combatants.

iWar as a Driver of Technology Innovation.

Operationalizing the doctrines of iWar depended 

upon the concurrent development of several enabling 

technologies, many of which had not been used on the 

battlefield prior to 9/11. These included innovations 

in persistent surveillance, standoff precision strike, 

biometrics and expeditionary forensics, and advanced 

analytical and information management tools designed for sharing identity data across the entire national security enterprise. These technologies helped 

to create an informational base layer enabling the “patient and relentless man-hunting campaign” waged 

by the U.S. military against networked adversaries 

and individual combatants.50

The adoption of cutting-edge technologies for the 

waging of iWar has largely been consistent with previous episodes of military innovation defined by a deepseated American bias for techno-scientific approaches 

to strategy. Much of the intellectual genealogy for 

iWar can be traced back to concepts formed by postWorld War II era cybernetics and the effort to apply 

engineering and advanced mathematical techniques 

to national security problems.51 Cybernetics provided the foundational thinking for later evolutions in 

warfighting theory such as network-centric warfare, 

revolution in military affairs (RMA) technologies, 

and effects-based operations.52 The use of quantitative 

data and advanced analytics in support of precision 

targeting has been the consistent theme among the 

evolution of these warfighting theories, including in 

the rise of iWar.
25

Similar thinking also influenced America’s last 

experience of waging counterinsurgency during Vietnam with the application of scientific tools and quantitative methods that ultimately failed to deliver desired 

strategic outcomes.53 Even amidst this failure, General William Westmoreland and others prophesized a 

future battlefield where: 

enemy forces will be located, tracked, and targeted 

almost instantaneously through the use of data links, 

computer assisted intelligence evaluation, and...with 

surveillance devices that can continually track the 

enemy.54

Such notions fed much of the thinking on military 

innovation during the 1970s and 1980s which focused 

on creating a precision strike revolution with breakthrough technologies in the areas of reconnaissance, 

surveillance, and target acquisition. However, this 

revolution was directed primarily at the operational 

challenges of a Cold War battlefield and conventional 

force engagement. These efforts sought to prove the 

observation of General William E. DePuy, first Commander of the Army’s Training and Doctrine Command, that “what can be seen, can be hit. What can be 

hit, can be killed.”55

Many of the tools developed during this period 

of technical innovation contributed to the stunning 

results during the first Gulf War. They were applied 

again as part of the initial approach to operations in 

Afghanistan and Iraq based on the presumption that 

“surgical” campaigns would achieve similar strategic 

results without requiring large ground formations and 

lengthy occupations. However, in both campaigns, it 

soon became apparent that the emergence of a new 

kind of adversary would require a different set of tools 
26

and methods better suited for analyzing and targeting 

against networks and irregular combatants. This realization marked the beginning of a process to adapt a 

generation of legacy tools and technologies designed 

for the Cold War era to the new demands of fighting 

small cells and individual combatants. This involved 

repurposing existing technologies for new tasks, as 

well as integrating cutting-edge capabilities such as 

drones, biometrics, and expeditionary forensics into 

operational use.

Certainly most visible among these tools has been 

the use of unmanned aerial vehicles, or drones. Prior 

to 9/11, their operational use was limited primarily to 

reconnaissance missions in the Balkans and Afghanistan. They were not successfully tested as a weapons 

platform until early-2001, then rapidly adapted to kinetic targeting in Afghanistan. Early in the campaign, 

General Tommy Franks called the Predator “my most 

capable sensor in hunting down and killing al Qaeda 

and Taliban leadership.”56 The inventory of these platforms increased 40-fold between 2002 and 2010, with 

dramatic expansion in operational use.57 For example, 

during all of 2007, there was a total of 74 military 

drone strikes in Afghanistan; however, by 2012, that 

number averaged 33 strikes per month.

58

Over time, a combination of improved sensors and 

software packages has enabled analysts to “recognize 

and categorize humans and human-made objects,” 

providing unprecedented real-time surveillance and 

detailed granularity for tracking individual combatants.59 Perhaps more significantly has been the degree to which targeted drone strikes “have gone from 

a relative rarity to a relatively common practice” as 

part of U.S. counterterrorism strategy.60 The first example of this was the 2001 Predator strike that killed 
27

Mohammed Atef, a senior al-Qaeda member linked to 

the 1998 U.S. embassy bombings in Tanzania and Kenya.61 Since then, these strikes have become a key tactic used against al-Qaeda, the Taliban, and affiliated 

groups in Pakistan since 2007, in Yemen since 2009, 

and in Somalia since 2006.62 Unclassified estimates 

suggest that over 98 percent of nonbattlefield targeted 

killings conducted by the United States over the last 

decade have been launched from these platforms.63

While drone warfare has captured the lion’s share 

of public attention, arguably the operationalization of 

biometrics and expeditionary forensics offers a more 

vivid demonstration of how technology trends have 

evolved in support of individualized warfare and 

identity-based targeting. These innovations emerged 

directly from the operational challenges of counterinsurgency and the need to identify and distinguish 

adversaries from the larger population. This requirement led to the rapid development and use of tactical 

biometric systems on the battlefield in both Iraq and 

Afghanistan.64

As with drone technology, there had been no significant operational use of biometrics by the U.S. military prior to 9/11. The Army’s biometric development 

program only began in 1999 at the Battle Command 

Battle Laboratory, and by 2001 had produced the first 

iteration of the Biometric Automated Toolset (BAT), 

a multi-modal (fingerprint, iris, and face) system for 

collecting, matching, and storing biometric and personally identifying information. The technology was 

initially field tested in the Balkans for identifying and 

tracking local national workers accessing U.S. installations; however, soon after the start of combat operations in Afghanistan and Iraq, military planners recognized the urgent need for identity management tools 
28

on the battlefield. Given early operational challenges, 

DoD quickly recognized biometric identification “as 

a basic warfighting capability, especially when fighting insurgent enemies who hide among the civilian 

populations.”65

The BAT prototype was fielded to Joint Special 

Operations Command in Afghanistan in early-2002 

for enrolling persons of interests. By 2003, it was being 

used in Iraq as well, first at the Abu Ghraib detention 

facility for detainee management and later for biometrically linking detainees to interrogation reporting.66

BAT was also issued to Marine Corps units and used 

during the resettlement of Fallujah following major 

combat operations in 2004. As residents reentered the 

city, U.S. forces screened and biometrically registered 

all males, collecting names, birth dates, places of birth, 

religious affiliations, height, weight, hair and eye color, fingerprint and iris scans, and digital photographs. 

This data was compared against identities of known 

and suspected insurgents and linked to biometric 

identification cards that were used to monitor the flow 

of male residents into and out of the city.67 A separate 

incident that same month in Mosul starkly demonstrated the pressing need to verify and track identities on the battlefield after a military dining facility 

was bombed by an insurgent with access to the base, 

killing 22 people. Identity management tools became 

an operational necessity in an environment where 

an anonymous enemy circulated freely just outside 

the gates.

The operational use of biometrics expanded rapidly as the United States shifted toward a populationcentral counterinsurgency strategy in Iraq and was a 

critical tool during the “surge” period as one of the 

primary means of separating insurgents from the 
29

larger population. Multi-modal or “13-point biometric” collection (10 fingers, two irises, and one face) became a standard feature of encounters with the local 

population during combat patrols. As the identity database grew over time, biometrics, linked with operational forensics, were used extensively for analyzing 

and penetrating cells employing IEDs against coalition forces. For example, during 2007-08, more than 

1,700 individuals were biometrically linked to forensic 

evidence relating to the manufacture and use of these 

weapons against coalition forces.68 The same year, information contained in biometric databases revealed 

that numerous Iraqi personnel applying for selection 

into the Iraqi Police Academy had identities matching 

previously-detained terrorists and insurgent suspects, 

and even several individuals with felony records in 

the United States.69 By 2011, at the end of operations 

in Iraq, the United States had compiled a biometric 

database containing some three million files on Iraqi 

citizens.70

Similarly in Afghanistan, over 7,000 biometric collection devices were employed in support of detention operations, execution of high-risk warrants, and 

targeted raids against named insurgents. Between 

2004 and 2011, U.S. forces made 1.6 million biometric enrollments on more than 1.1 million individuals 

—roughly equivalent to one of every six fighting-age 

males, and used this data to identify positively more 

than 3,000 enemy combatants.71 This capability was 

particularly important in Afghanistan, a country with 

limited institutional capacity for identity verification; 

few birth certificates, drivers’ licenses, or citizenship 

documents. This situation was exacerbated by an 

active black market in forged identity papers. 
30

The integration of BEI into the targeting cycle degraded insurgent leadership capability to hide among 

the local population and exposed lower-level cell 

structures. Coalition and Afghan forces also regularly 

employed biometric watch lists and “be on the lookout” messages which led to the “arrests, warrants, and 

the removal of insurgent anonymity.”72 By the end of 

active combat operations in Afghanistan, the United 

States had placed some 33,000 individuals’ identities 

on the biometrically enabled watch list.73 One vivid 

example demonstrating the power of such information came in 2011 after some 500 Taliban prisoners 

escaped from Kandahar’s Sarposa prison. All of the 

detainees had previously undergone biometric enrollment, and within 1 month, some 30 individuals were 

recaptured just as a result of random biometric checks 

in the local area.74

One outcome of this extensive biometric data collection on the battlefield has been an improved capability to monitor combatant identities and track 

cases of fighter recidivism. Estimates of recidivism 

rates have been somewhat variable due to the challenge of verification, classification, and how the term 

“reengagement” is defined; however, there is a general acknowledgment that some nontrivial portion 

of released and escaped detainees historically have 

returned to active fighting.75 For example, toward the 

end of U.S. combat operations in Iraq, approximately 

10 percent of all biometric matches made by forensic 

linkage to explosives device materials revealed an association with individuals previously released from 

detention. More recently, Abu Bakr al-Baghdadi, the 

“emir” of the Islamic State, emerged as perhaps the 

most famous recidivist. He was originally captured by 

U.S. forces in 2004 near Fallujah and later spent time 
31

in U.S. detainment facilities at Camps Bucca and Adder before his release. Separately, a recent Iraqi government report estimated that some 17 of the 25 most 

important Islamic State leaders also spent time in U.S. 

facilities between 2004 and 2011.76 Biometric technologies have offered one of the few effective means 

of tracking this recidivism and monitoring foreign 

fighter flows through zones of conflict.

Since their introduction in Iraq and Afghanistan, 

biometric technologies have spread to other theaters 

with similar operational challenges of identity verification, such as counterpiracy operations in East 

Africa.77 As an identity intelligence specialist at the 

Army’s Training and Doctrine Command explained, 

“biometrics puts a uniform on the enemy” and enables the categorization of actors even in the absence 

of traditional status-based signatures.78 Recognizing 

the operational value of this technology, DoD recently 

designated biometrics as a core function and directed 

combatant commands to integrate biometrics into 

mission planning.79 The U.S. Army, in particular, has 

continued pushing for integration of new modalities, 

most recently with the development of Voice Identity Biometric Exploitation Services (VIBES), a tool for 

standoff voice identification, tracking of high-value 

individuals, and biometrically-enabled network link 

analysis.80 Other efforts include development of newer and more portable versions of handheld devices for 

biometric collection, as well as an ongoing upgrade to 

the Automated Biometric Identification System (ABIS) 

database for managing and sharing biometric data 

across the government.81

Expeditionary forensics is another area where a 

new military technology evolved rapidly in response 

to the operational demands of waging iWar. Over 
32

the last decade, forensic tools and analysis have been 

central to evidenced based targeting methods to “individualize, identify, associate, and scientifically link 

people, places, things, intentions, activities, organizations, and events.” In late-2004, U.S. forces in Iraq 

pioneered the use of expeditionary forensics for identifying IED makers and networks. This began with 

the National Ground Intelligence Center expanding 

the existing capabilities of the Combined Explosives 

Exploitation Cells, primarily focused on the technical 

intelligence and chemical analysis of weapons. 

The forward presence of these labs “greatly increased the qualitative and quantitative capacity of 

U.S. forces to recognize, preserve, and analyze forensic materials in-theater” and support identity-based 

targeting methods against insurgent networks.82 By 

2006, these facilities were employed in both Iraq and 

Afghanistan, including new capabilities for ammunition, clothing, latent fingerprints, and deoxyribonucleic acid (DNA) analysis, as well as digital media forensics. By 2010, the United States had deployed a total 

of seven such expeditionary laboratories to Iraq and 

eight to Afghanistan.83 During that year alone, they 

enabled operational targeting and capture of over 700 

high-value individuals associated with IED networks, 

suspected terrorists, and other criminal actors.84

Particularly in the case of DNA analysis, prior to 

Iraq and Afghanistan there was very limited operational use of this technology beyond its traditional role 

supporting criminal investigations. Though highly 

accurate, the existing technologies for DNA analysis 

were slow and expensive, therefore generally limited 

to laboratory use. This situation changed dramatically 

over the last decade with a focused effort by the military to modularize these capabilities into mobile labs 
33

working in direct support of operational commanders. Initially in Iraq, these were used primarily in a 

criminal forensics role for investigating extrajudicial 

killings; however, over time, the mission expanded 

to analysis of samples recovered from torture houses, 

terrorist caches, and identification of high-value individuals killed or captured during targeted raids. 

By 2008, U.S. forces reportedly had gathered some 

80,000 individual DNA samples in support of military 

law enforcement and intelligence requirements.85 By 

that time, expeditionary labs were capable of processing high-priority samples in less than 24 hours.86 Perhaps their most famous application came when a U.S. 

military forensics laboratory in Afghanistan analyzed 

DNA from Osama bin Laden’s corpse to confirm his 

identify shortly after the raid by a U.S. Navy Sea, Air, 

Land team in May of 2011.87 More recently, U.S. Special 

Operations forces have been field testing rapid DNA 

readers in forward locations, using commercial devices weighing around 60 pounds that deliver results in 

less than 90 minutes, a significant improvement over 

technologies available just a few years ago.88

Over the last decade, the emerging technologies 

supporting forensic analysis became critical to “evidence-based” targeting approaches used as part of 

counterterrorism and counterinsurgency strategies, 

as well as contributing to stability operations, local 

governance, and reestablishing rule of law. During 

the transition period from combat operations in Iraq 

and Afghanistan, the indigenous criminal justice system became the only means of removing insurgents 

and terrorists from the battlefield, and forensic science became a primary tool enabling U.S. forces to 

“accurately identify subjects of interest and provide 

irrefutable links to criminal and terrorist activities” 
34

in a manner consistent with recognized evidentiary 

standards.89 For example, during 2012, U.S. forces provided forensic evidentiary analysis in support of some 

120 Afghan court cases linking enemy combatants to 

latent fingerprints and DNA evidence, resulting in a 

97 percent conviction rate in these cases.90 As a practical matter, this also meant that targeting operations 

frequently were tailored for the specific purpose of 

obtaining evidentiary material in support of criminal 

proceedings and conviction under rules of civil law.91

One study on the use of forensics and biometrics 

during these campaigns noted how these technologies 

directly supported “precise fires to shape the operational environment, including supply chain interdiction, counterthreat finance operations, information 

operations, cache destruction, and the capture of highvalue individuals.”92 As one example, the U.S. task 

force responsible for detainee operations in Afghanistan estimated that some 70 percent of individual targets captured on the battlefield were identified with 

the help of biometrics and forensics technologies.93

Separately, a U.S. Government Accountability Office 

study concluded that the conflicts in Iraq and Afghanistan had revolutionized expeditionary forensics, 

specifically the use of latent fingerprints and DNA 

analysis in support of military operations.94

The rapid growth of biometrics, forensics, and 

large amounts of incoming data from persistent surveillance platforms all created a new challenge for 

analysts—specifically the task of sorting through an 

unprecedented amount of battlefield information 

gathered at the tactical level. According to one report, this deluge of sensor data was making it “nearly 

impossible to track and identify suspicious activities 

and potential security threats solely through human 
35

analytical processes.”95 A related problem was in processing and correlating multiple streams of unstructured data such as patrol reports, cell phone numbers, 

biographic data, document and media files, biometric 

signatures, and forensic evidence. The enormity of this 

challenge made information management a singularly 

critical task in the waging of iWar. For this reason, the 

development of new tools and methods for organizing, storing, analyzing, and disseminating this data 

became a major, if underappreciated, achievement of 

military-technical innovation over the last decade.

As U.S. forces began collecting large amounts of 

biometric data on the battlefield, there was an urgent 

need for an authoritative database to process, store, 

and match biometric results. This included the ability to share identity information between widely dispersed tactical forces, as well as among other DoD 

elements, the intelligence community, and domestic 

law enforcement. This led to the initial prototype design in 2004 for what would eventually become the 

ABIS, the military’s centralized multi-modal biometric data repository which is able to process, store, and 

produce match results for fingerprint, palm print, iris 

scans, and facial scans from persons of interest. This 

system was designed to support a diverse range of 

operational functions, including detainee operations, 

force protection, counterterrorism screening, special 

operations targeting, and other intelligence requirements.96 Importantly, it was structured for the capability to share data with other DoD, interagency, and 

multinational partners. Following the initial fielding 

of ABIS a Biometrically Enabled Watchlist feature was 

integrated that enabled analysts to highlight personof-interest records and provide specific disposition 

instructions for each individual of interest.97
36

As DoD began collecting enormous amounts of 

identity data on the battlefield, there was also a need 

for improved processing and analytical capabilities to 

sort through the growing repository data and leverage SNA methodologies in support of targeting. This 

led to the adaptation of analytic tools such as Analyst 

Notebook, part of the Distributed Common Ground 

System, and the controversial Palantir platform used 

by military units in Afghanistan and elsewhere. 

Palantir was used for network analysis and data visualization and was based on commercial applications 

originally developed at PayPal for use against cybercrime networks and in detecting fraudulent financial 

transactions.98 The software has also been used to 

analyze urban crime patterns, mapping Mexican drug 

cartel activity, and recently for deconstructing a Syrian suicide-bombing network by analyzing hundreds 

of al-Qaeda personnel records captured by U.S. forces 

in Iraq.99

With the increasing need to identify, track, and target individuals across the battlefield, other specialized 

tools were developed that more specifically focused 

on identity management and attribute correlation. 

One such application, the Special Operations Forces 

Exploitation portal, was designed for collection, transmission, and enrollment of biometric, forensic, and 

document and media data in support of identity discovery and analysis. Within the Army’s Distributed 

Common Ground System architecture, the Counterintelligence and Human Intelligence Automated Reporting and Collection Systems is a platform designed 

for managing and analyzing identity based information derived from interrogations, biometric collection, 

and document exploitation operations.100 The DoD’s 

Biometrics Identity Intelligence Resource is another 
37

tool allowing intelligence specialists to analyze and 

fuse biometrics information stored within ABIS and 

provide this information to the users in the field. This 

includes a capability for correlating identity attributes 

with situational, contextual, and temporal data associated with biometric collection events.101

The evolution of these systems over the last decade highlights the central importance of information management and data analysis to the waging of 

iWar. While originally focused on identity verification 

and force protection, over time these biometric tools 

were adapted for direct support of targeting. Somewhat less recognized has been the degree to which 

these military technologies and methods have been 

integrated into the larger national security enterprise, 

in particular their contribution to the identity-based 

screening approaches that have become the central 

component of how iWar is being waged at home as 

well as abroad. The migration of these tools from the 

battlefield to the home front has been combined with 

an unprecedented expansion of information sharing 

between the military, intelligence, and domestic law 

enforcement communities.

The current effort of tracking and monitoring 

foreign fighters operating in Syria demonstrates the 

degree to which iWar has become a whole-of-government enterprise, dependent on data sharing, operational collaboration, and technical integration across 

the entire national security apparatus. This strategy 

has focused primarily on monitoring, interdicting, and 

investigating potential threats to the homeland, most 

notably through the screening of foreign travelers 

entering at border control points, but also indentification of “home grown” extremists potentially affiliated 

with international terrorism. As with military target-
38

ing in Iraq and Afghanistan, over the last decade these 

domestic security programs have become increasingly 

intelligence-driven, identity-based, and dependent 

on the use of advanced information management 

technologies.

This first line of homeland defense is comprised of 

a variety of immigration screening programs directed 

primarily at transportation networks and ports of entry. Identity management has become the central element of this risk-based screening process based on 

biographic information and biometric checks of foreign travelers seeking to enter the United States. This 

information is held within a network of interoperable 

databases that enable government agents to access 

biographic, biometric, and, in some cases, DNA data, 

held by the Departments of Justice, State, and Defense, 

and the intelligence community.102 Two examples of 

this frontline identity-based screening architecture 

are the Electronic System for Travel Authorization 

(ESTA) and the Visitor and Immigration Status Indicator Technology (US-VISIT) Program. ESTA is a 

name-based biographic database used to determine 

traveler eligibility and screen potential risks from 

foreign nationals prior to entering the United States. 

ESTA is complimented by the US-VISIT Program that 

includes multiple databases of biographical and biometric data used to verify identity and cross check 

travelers against national-level watch lists.103

Many of the technologies and methods being applied against these new homeland security challenges 

evolved in parallel with those employed in Iraq and 

Afghanistan, often sharing the same data on potential 

threats. One notable example is the Federal Bureau of 

Investigation’s (FBI) Next Generation Identification 

(NGI) system, the bureau’s recently updated biometric 
39

repository containing over 100 million fingerprint records and other biometric files.104 This system includes 

a facial recognition pilot project with a capability for 

searching criminal mug shots, as well as planned integration of palm print, iris, scars, marks, tattoos, and 

latent fingerprints into the searchable national database. Similarly, the Department of Homeland Security 

(DHS) maintains its own automated biometric identification system (IDENT), a repository of biometric 

and biographic data used for national security, law 

enforcement, immigration, and border management. 

IDENT supports homeland security requirements 

by providing iris and facial matching capabilities to 

the U.S. Border Patrol and other entities. According 

to a recent estimate, IDENT processes some 300,000 

transactions per day, drawing on a database of some 

173 million unique identities.105 This database is also 

linked to the U.S. Coast Guard’s Biometrics at Sea System program that is comprised of portable biometric 

systems deployed on 23 cutters used for identifying 

unknown individuals in the maritime environment, 

including suspected terrorists, criminal suspects, and 

other individuals of interest.106

The analytical power derived from this interagency data integration was recently demonstrated when 

the FBI identified a masked Islamic State terrorist 

known as “Jihadi John,” who had appeared in videos 

depicting the killing of several hostages, including an 

American citizen. Based on image and voice data, the 

FBI reportedly identified the individual with the assistance of DoD and foreign intelligence partners. The 

FBI’s Assistant Director for the Criminal Justice Information Services Division noted that: 

the FBI absolutely could not do its mission if we didn’t 

have interoperability with DHS and interoperability 
40

with DOD, because they have holdings in their biometric repositories . . . data that may be a piece that we 

don’t have.107

Department of State and overseas consular officers 

also use similar biometric tools and biographic databases, such as the Consular Consolidated Database 

(CCD) for screening all U.S. visa applicants. In 2001 

the CCD began storing photographs of all visa applicants, and since 2007 has included 10-digit biometric 

fingerprint scans that can be cross-referenced against 

data contained in the DHS, FBI, and DoD systems.108

This database reportedly contains some 143 million 

biographic records of visa applications dating back to 

the mid-1990s.109 As part of visa processing, consular 

officers are required to check applicant backgrounds 

against the Consular Lookout and Support System 

that lists individuals of concern and those who have 

been previously denied visas. This database contains 

over 42.5 million records, approximately 70 percent of 

which come from other agencies, including DHS, the 

FBI, and the Drug Enforcement Administration.110

All of the major defense, intelligence community, 

and homeland security screening programs are designed to leverage identity information contained in 

the Terrorism Screening Database that is maintained 

by the FBI’s Terrorist Screening Center (TSC). In the 

first 2 years after its inauguration in 2003, the TSC reportedly issued some 6,000 alerts to U.S. security services based on known terrorist identities or individuals suspected of foreign terror connections.111 A recent 

expansion of the architecture now enables an automated review of all nonimmigrant visa applicants to 

identify any potential terrorism connections contained 

in the Terrorist Identities Datamart Environment 
41

(TIDE) and other national databases.112 In some cases, 

this also includes information obtained from foreign 

partners through reciprocal data sharing agreements 

pertaining to lost and stolen passports, reservation 

data, biometric information, and even DNA profiles 

identifying possible connections between known 

terrorists and unknown associates.113

A separate but integrated database, TIDE, is also 

maintained by the National Counterterrorism Center. 

This dataset includes all-source and classified information provided by the FBI, intelligence community, and 

DoD components, and has become the nation’s consolidated record for screening, detection, and interdiction of known and suspected terrorists. Extracts from 

the TIDE are used to compile the watch lists for various domestic security programs including the Transportation Security Administration’s Secure Flight program that vets passenger identities from all in-bound 

and outbound manifests from U.S. airports.114 As of 

the beginning of 2014, TIDE contained record files 

on some 1.1 million individuals.115 According to unconfirmed press reporting, the database also includes 

some 47,000 identities cross-listed on so-called “no-fly 

lists,” as well as some 860,000 biometric files.116 During the years of active combat operations in Iraq and 

Afghanistan, the U.S. military was the dominant contributor of biometric and identity information into the 

TIDE database; however, unconfirmed press reports 

suggests that the balance has shifted somewhat in recent years as the intelligence community and federal 

and local law enforcement all contribute new identity 

information and provide watch list nominations.117

Similar information architecture has also evolved 

around the collection and storage of DNA data. Traditionally, this data has been used to support law 
42

enforcement investigations; however, it is increasingly being applied to national security, defense, and 

intelligence requirements. A 2007 Defense Science 

Board Task Force on Biometrics described a DoD forensic counterterrorism concept evolving after 9/11 to 

develop “a secure repository and interactive database, 

which will focus on archiving, retrieving, and interpreting bio-molecular data for the identification and 

tracking of terrorist suspects.” Within DoD, the Armed 

Forces DNA Identification Laboratory has maintained 

such a “red force” DNA database, including profiles 

from non-U.S. battlefield detainees, unknown DNA 

gathered as a result of raids, and forensic evidence 

collected at other sensitive sites.118 By 2007, the Joint 

Federal Agencies Intelligence DNA Database reportedly contained over 15,000 DNA profiles, with twice 

as many samples waiting to be processed. During the 

years of active combat operations in Iraq and Afghanistan, DoD was the primary customer for this analysis; 

however, the database has also supported the intelligence community, domestic law enforcement, and FBI 

investigations for counter-IED efforts.119

The U.S. Army’s Criminal Investigation Laboratory (USACIL) also provides DNA data that is entered 

into the FBI’s Combined DNA Index System (CODIS) 

that allows federal, state, and local crime laboratories 

to store, search, and share DNA profiles electronically. 

As of late-2014, the FBI’s CODIS database contained 

over 11 million offender profiles, two million arrestee 

profiles, and nearly 600,000 forensic profiles.120 Internationally, some 30 countries have similar CODIS-like 

databases, many using compatible technical standards and formatting making it possible for profiles to 

be compared between national databases.121 USACIL 

also supports the National DNA Index System (part of 
43

CODIS) by providing DNA profiles of interest relating 

to law enforcement concerns, detainees and counterterrorism identities. However, this database is predominantly a “blue force” repository for specimens 

used in mortuary affairs and criminal investigations, 

and not sufficiently populated with target profiles to 

make it tactically responsive for operational targeting. 

One program manager described the current process 

for rapid DNA as being “about where fingerprints 

were 10 or 12 years ago.”122

In addition to improved data integration and analytical tools, other recent advancements have helped 

to make biometric and forensics technologies operationally viable over the last decade, including miniaturization and portability of collection devices. The 

prototype U.S. biometric system originally developed 

in 1999 by the Army’s Battle Command Laboratory 

was developed principally for use at fixed facilities to 

manage base access in the Balkans. The original technology was not highly mobile and lacked real-time 

connectivity to remote servers for data transfer and 

watch list updates. The operational demands encountered early in Iraq led to efforts to improve miniaturization, portability, and remote connectivity as seen 

in subsequent iterations of the BAT and later in the 

Handheld Interagency Identity Detection Equipment 

system deployed in 2007.

Forensic science followed a similar trajectory with 

the development of self-contained, mobile forensics 

laboratories. These modular and scalable “labs-in-abox” were designed specifically for tactical ground 

transport and inter-theater lift with deployed technicians to support forensic analysis at forward locations. 

The labs offered a full range of exploitation capabilities that were generally only available at fixed labora-
44

tory facilities prior to 9/11. These new expeditionary 

capabilities included analysis of latent prints, DNA, 

firearms and toolmarks, trace chemicals, and document and media exploitation.123

As these technologies demonstrated their utility for identity-based screening and targeting in Iraq 

and Afghanistan, they began migrating into use for 

domestic law enforcement, immigration, and border 

control functions. For example, the FBI’s new biometric system has been designed to enhance connectivity 

down to local law enforcement authorities with mobile devices for fingerprint collection and real-time 

feedback on matches against the FBI’s criminal database. Similarly, its Interstate Photo System now uses 

upgraded facial recognition technology for searching 

suspect photos against images for millions of known 

criminal identities.124 The U.S. Customs and Border 

Protection specifically studied lessons learned in Iraq 

and Afghanistan in order to apply similar tools and 

methods for its identity-based screening programs and 

operations at remote locations along the border. This 

led to a program enabling mobile biometric collection 

to have real-time matching against national level databases of criminal and terrorist identities.125 The agency 

has also begun a pilot program using new facial recognition technology for comparing passport photos 

with travelers’ faces in order to detect illegitimate 

documents.126

In sum, over the last decade, the rise of iWar has 

introduced a new operational paradigm and approach 

to warfare characterized by three distinct elements: 

individualization, identity, and information. This 

transformation has been based on the systematic disaggregation of national security threats down to the 

lowest tactical level and a strategic prioritization on 

countering emerging threats from nonstate actors and 
45

individual combatants. This operational focus has 

driven a decade of doctrinal and technical innovation 

focused on identity-base screening and highly personalized forms of military targeting. This new mode of 

warfare rests upon a foundational layer of information technology that has enabled an unprecedented 

level of data collection and information sharing across 

the entire U.S. national security apparatus. This phenomena reflects a larger organizational dynamic 

closely related to the iWar paradigm, specifically how 

this form of warfare has led to a gradual erosion of 

traditional boundaries separating military operations, 

foreign intelligence, and domestic security functions. 

Additionally, as U.S. national security strategy has 

focused on the threats posed by individual combatants and nonstate actors, it has created the political 

context for perpetual warfare with few discernable 

geographic or temporal boundaries. The next section 

briefly examines the rise of iWar within the context of 

these policy decisions and considers the implications 

for the future of national security strategy.

iWar as Policy Choice.

The new doctrines and supporting technologies 

provided the means and methods of iWar; however, 

ultimately this paradigm shift resulted from policy 

choices and strategic decisions made in response to 

perceptions about a changing threat environment. 

The 2001 Authorization for Use of Military Force (AUMF) 

established the initial legal context for waging war 

against geographically dispersed networks and individuals. Using broad language, it authorizes the 

use of force against “nations, organizations, or persons.”127 Central Intelligence Agency (CIA) Director 
46

John Brennan recently articulated how this original 

AUMF construct has been applied against a broad 

range of threats, noting that, “in this armed conflict, 

individuals who are part of al-Qaida or its associated 

forces are legitimate military targets.”128 Over time, the 

use of “targeted killings” against al-Qaeda leadership 

targets has evolved as a centerpiece of the U.S. counterterrorism approach and has been used extensively 

in Pakistan, Yemen, Libya, and elsewhere. However, 

by any estimation, this approach has expanded far 

deeper than limited targeting of high-level leadership 

figures and now represents a new mode of warfare 

being waged by means of “precise attacks against 

individuals.”129

Arguably, the use of such focused, individualized 

approaches to counterterrorism has been the logical 

path for a liberal democracy dealing with the threat 

of terrorism, while also seeking to balance civil liberties in a pluralistic society. Public discomfort with 

profiling techniques used in the aftermath of 9/11 created political pressure for methods focused more specifically on individuals with legitimate connections 

to terrorism rather than categorical measures applied 

against entire suspect groups (racial, ethnic, religious, 

or otherwise). More recently, popular concern over 

the broad application of domestic surveillance by the 

National Security Agency (NSA) suggests a similar 

worry with dragnet-like approaches involving bulk 

signals collection. Nevertheless, Americans generally 

have expressed few reservations with focused intelligence gathering, even lethal targeting, when based 

on evidentiary approaches and presumptions of culpability.130 Thus, as a matter of policy, the waging of 

iWar has presented few political liabilities for successive administrations.
47

Similar thinking seems behind the general public 

acquiescence over “watch listing” methods designed 

to screen and identify individuals with known or suspected ties to terrorism.131 For example, the 2008 National Security Presidential Directive 59, authorizing biometric collection and information sharing on persons 

posing potential threats to national security, drew relatively little public attention.132 Dennis C. Blair, the Director of National Intelligence, perhaps best captured 

the new logic of personalized risk assessment when he 

observed how the threat of terrorism has changed the 

approach to strategic warning, noting that now there 

are “names and faces to go with that warning.”133 In 

general, Americans appear somewhat resigned to 

such identity-based counterterrorism approaches as 

long as they appear directed at potential threats and 

not applied indiscriminately.

Perhaps ironically, foreign public opinion and diplomatic pressure has also played a role on pushing 

the United States toward the iWar paradigm and the 

need for greater discrimination and personalization 

of counterterrorism efforts. One noteworthy example 

has been the broad international condemnation of U.S. 

“signature strikes” directed against patterns of adversary behavior rather than specific individuals.134 This 

approach closely resembles conventional targeting 

methods applied against formations, equipment, and 

facilities, where technical signatures provide generally 

reliable categorization of intended targets. However, 

the use of this technique against irregular or personnel 

targets has resulted in numerous incidents of misidentification and unintended civilian casualties, including the deaths of U.S. citizens and hostages.135 Even 

under the best of circumstances, this mode of targeting can be inherently challenging. As one official with 
48

Special Operations Command recently noted, “When 

we’re trying to personally ID an individual, there is 

zero margin for error.”136 When these errors do occur, 

they often cause significant political repercussions, as 

the United States has experienced in Pakistan and Yemen, as well as numerous incidents during the course 

of military operations in Iraq and Afghanistan.137

In response to these pressures, the Barack Obama 

administration has moved toward an increased use of 

“personality” strikes directed against confirmed identities in order to avoid blowback from unintended 

causalities. This process reportedly has been formalized through the creation of a “disposition matrix,” a 

dynamic, individualized, targeting database based on 

biographies, locations, associations, and operational 

profiles of high-value targets.138 However, some critics 

maintain that even these strikes have not been precise 

enough at identifying targeted individuals, particularly in areas such as Pakistan and Yemen where the 

United States does not have significant forces or intelligence capabilities on the ground. One expert on 

drone warfare noted that the frequency of unintended 

casualties sustained during these strikes highlights the 

fact that that even today, “most individuals killed are 

not on a kill list, and the government does not know 

their names.”139

Partly in response to these criticisms, the administration has even suggested a policy preference for 

the capture and prosecution of individual terrorism 

suspects, when feasible.140 Given continuing concerns 

over long-term battlefield detention of irregular forces, this aspect of iWar as policy highlights the inherent 

tensions between traditional warfighting activities and 

law enforcement functions. In recent years, identitybased targeting packages have come to resemble ar-
49

rest warrants more than Cold War-era targeting folders. Particularly during the later phases of operations 

in Iraq and Afghanistan, high-value targeting evolved 

toward “evidence-based” approaches, dependent 

upon identity verification and forensic science to support probable cause-like adjudications and actionable 

intelligence. One observer noted how the F3EAD process gradually evolved into a “police-like investigate, 

arrest, and convict” mode of nonlethal targeting.141

Particularly as counterinsurgency strategy gained 

momentum, observers noted how soldiers on patrol 

began to “behave almost like city cops,” doing “biometric scans and turning in evidence bags with each 

detainee.”142 These examples seem to support speculation by Harvard scholar Gabriella Blum that the individualization of warfare may utterly transform the 

nature of armed conflict, moving it more toward a policing model with greater focus on individual harms, 

individual victims, and individual culpability.143

From a policy perspective, the targeting paradigm 

of iWar has also created a challenge for strategic assessment and useful measures of effectiveness. During 

the history of modern warfare, a variety of measures 

have been applied as proxies for evaluating different 

operational approaches, warfighting theories, and 

strategic decisions. World War II offered a relatively 

straightforward quantitative template supported 

by an endless supply of statistics on the numbers of 

bombs dropped, convoys and factories destroyed, 

square miles of territory seized, soldiers captured, and 

units defeated on the battlefield. Such measures were 

well suited to an industrial-age conflict where the 

central strategic challenges often concerned issues of 

time, distance, and production—all problems generally reducible to structured analysis and quantitative 

assessment.144 Conversely, Vietnam produced a per-
50

version of such measures, particularly as enemy body 

counts emerged as a proxy measure for operational 

success in the absence of a clear political-military 

end state. 

The iWar paradigm has replaced generic body 

counts of Vietnam with a modified version of effectsbased assessment based on highly relativistic impact 

analysis. Various approaches have weighed factors 

such as an individual fighter’s operational profile, 

unique technical skills, network centrality, and an 

actor’s influence within the larger organizational network. These techniques have deep conceptual grounding in Social Network Analysis theory; however, they 

still rely on highly subjective measures that are difficult to apply as a centerpiece of campaign assessment. 

For example, one phenomena that emerged early in 

the Iraq war was the infamous “personality identification playing cards” issued to U.S. troops that depicted 

high-ranking members of Saddam Hussein’s government and senior Ba’ath Party members. The cards 

soon developed into something of an informal yardstick for operational progress on the battlefield as key 

leaders were killed or captured. Attention remained 

focused on such measures, even as it became abundantly clear that they had no particular relevance to 

the evolving security dynamics on the ground.145 Over 

time, similar methods have evolved for measuring the 

effects of counterterrorism strikes against adversary 

networks, applying qualitative assessments to gauge 

the operational impact of killing or capturing specific 

“high-value individuals.” However, as with the body 

counts of Vietnam, there is an inherent artifice imbedded into the process. No matter how many times the 

“al-Shabaab number two” is killed, there may always 

be another “al-Shabaab number two.” Conversely, 

Berlin fell only once.
51

The issue of operational assessment raises a larger 

issue in terms of evaluating the iWar paradigm both 

as policy choice and as a component of military strategy. While there is little debate as to the awe-inspiring 

tactical efficiency of U.S. techniques for identifying 

and targeting individual combatants on the battlefield, what is less certain is whether these methods 

aggregate into effective strategies for achieving larger 

political objectives. The perpetual regeneration of 

terrorist threats inside Pakistan, Yemen, and Somalia offers little evidence that identity-based targeting 

has been fully successful as a centerpiece of counterterrorism strategy. Likewise, the deteriorating conditions in Iraq and Afghanistan suggest limits as to what 

these approaches can deliver as a major component 

of counterinsurgency. Conversely, one can make a 

strong claim that the combination of these techniques 

has contributed, in part or whole, to protect the homeland successfully from a major terrorist attack since 

9/11 by identifying and dissuading individual threats 

and disrupting their networks. The inherent ambiguity in the data raises the more difficult question as 

to whether one can evaluate effectively the utility of 

specific tactics and tools separately from the overall 

strategic outcomes they produce. As H. R. McMaster, 

Director of the Army’s Capabilities and Integration 

Center, recently cautioned, “Targeting does not equal 

strategy.”146

President Obama observed during a recent address 

to the National Defense University that “We must define the nature and scope of this struggle, or else it will 

define us.”147 Indeed, this has largely been the case for 

the U.S. national security establishment since 9/11. 

Over the last 15 years, these bureaucracies have undergone a revolution in form, function, and focus that 
52

has moved them toward a model of warfare far different from that of the Cold War era. These entities have 

developed new doctrines, technologies, and analytical 

methods that reflect a significant reorientation of national security priorities based on the task of defeating 

networked adversaries and targeting individual combatants. As one senior U.S. officer recently noted, the 

task of “putting warheads to foreheads” has become 

a core military function and arguably the reflection of 

a new model of state security centered on defeating 

threats from nonstate actors, networked adversaries, 

and individual combatants.

By all indications, some form of iWar will persist 

as a model of state security and American warfare 

into the future. Contemporary security challenges 

are likely to be characterized by operations in areas 

of poor governance with weak identity regimes, and 

against adversaries determined to use anonymity 

to operational advantage. Meanwhile, threats from 

transnational terrorists show little sign of abating. For 

these reasons, the operational relevance of identity on 

the modern battlefield and along the borders will only 

grow in importance.

iWar as Case Study of Military Innovation.

The rise of iWar offers a useful case study of military 

innovation. The iWar paradigm evolved as the result 

of a very specific national security challenge encountered after 9/11. Much of this doctrinal and technical 

innovation centered on the pressing need to identify, 

screen, and target individual combatants fighting as 

part of distributed networks. Initially, this task represented an operational challenge for which the U.S. 

national security apparatus was largely unprepared. 

Cold War-era doctrines and technologies were not 
53

conceived for the task of differentiating among individual combatants, or the challenge of screening and 

targeting them based on discrete identity attributes. 

Nor were pre-9/11 bureaucracies and warfighting 

strategies optimized to function across an unbounded 

battle space and against threats whose tactics did not 

conform to a clear differentiation between foreign military operations and domestic security functions. In 

this sense, the rise of iWar serves as a useful example 

for examining wartime innovation.

Scholars of military innovation generally look to 

several characteristics for evidence of substantive 

change. The first is that the process of innovation alters the manner in which military formations function 

in the field. The second is that these changes are significant in terms of scope and organizational impact. The 

third is that these reforms ultimately produce greater 

military effectiveness.148 By this standard, the rise of 

iWar has largely satisfied the first two elements; however, the third remains open to debate, depending on 

the scope of analysis and terms of measure. A separate 

but related question is whether iWar truly represents 

a new operational paradigm or simply a repackaging of old ideas implemented with new technologies. 

Arguably, one may identify iWar precursor elements 

in examples such as the CIA’s Phoenix Program in 

Vietnam and, more recently, with Israeli targeting 

methods used in Gaza and Lebanon. As with iWar, 

these examples also involved operational approaches 

based on network-centric analysis and highly focused, 

individualized targeting methods; however, several 

important aspects have arguably made the recent U.S. 

experience sui generis. 

Foremost among these differences has been the 

scope of application. Operations of the iWar paradigm 

have taken place on a global scale, enabled by a dra-
54

matic bureaucratic transformation that has integrated 

military operations, foreign intelligence activities, 

and domestic security functions more than any other 

time in American history. This strategy was based on 

an assortment of new technical innovations virtually 

unknown on the battlefield prior to 9/11, including 

drones, biometrics, expeditionary forensics, DNA 

analysis, and advanced information management 

tools, to name just a few. The application of these 

tools reflected a shift toward an information-centric 

view of warfare far different from the conventional 

force paradigm of the Cold War era. This transformation included an evolution in warfighting theory that 

introduced new methods into the doctrinal cannon 

such as Social Network Analysis and personalitybased targeting. All of these changes were centered on 

a fundamentally altered strategic logic that prioritized 

nonstate actors and individual combatants equally 

with the threats posed by state actors in the formation 

of national security policy. These changes also represented a subtle but profound shift in thinking about 

how military force should be applied against such 

threats, and in doing so, redefined the role of identity 

in modern warfare.

In analyzing these changes, some scholars suggest 

that the process of wartime innovation should be examined as a phenomena distinct from that of peacetime change.149 Arguably, the causal pathways of wartime innovation tend to be somewhat less complex as 

they are highly responsive to exigent circumstances. 

This is partly due to the fact that active conflict offers a 

laboratory for “natural experiments” where warfighting requirements are articulated explicitly in response 

to the actions of an immediate adversary rather than 

a hypothetical one. This situation provides a crucible 

for immediate tactical feedback, thus creating a pow-
55

erful dynamic for iterative design and rapid process 

improvement. These factors have the effect of sharpening how operational needs are defined and speeding the bureaucratic process of research, development, prototyping, and employment. However, rapid 

institutional change still requires an initial intellectual 

catalyst to serve as a conceptual driver to frame the 

strategy of innovation. Without some coherent idea 

that provides a common intellectual framework, the 

sum of innovation will inevitably be less than its parts.

In the case of iWar, this central idea was the realization that fighting against networks and individuals required an entirely different approach and toolset 

than methods optimized for conventional conflict. 

Once this overarching conceptual framework was established, the process of military innovation occurred 

rapidly across multiple spheres. The first sphere involved changes to warfighting theory that were translated into doctrinal design and higher-level concepts 

of operation. The second sphere involved the adoption 

of specialized technical tools and methods directly applied to tactical problems to create battlefield effects. 

A third sphere was a slower process of changing organizational behaviors, institutional values, and philosophical ideas about how a military service conceives 

of its role and defining functions within the larger 

national security enterprise.

At this point, iWar as an operational paradigm is 

at a crossroads. The challenge ahead will be to identify 

what aspects of the model remain relevant to future 

security challenges and overall U.S. defense priorities. 

This requires evaluating the lessons learned from the 

previous decade, developing supporting doctrines 

and technologies, establishing priorities for training, and then building the institutional foundations 

to support the desired capabilities. One of the most 
56

challenging aspects of this task is picking the correct 

technologies in the absence of a persistent operational 

challenge from a clearly defined adversary.

Inevitably, as the dynamics and imperatives of 

“peacetime” innovation take precedent, they will differ from those of wartime innovation. Other priorities 

are likely to grow in influence and consequently shape 

the debates over warfighting theory, doctrinal development, procurement strategies, and institutional 

roles. In the case of iWar, the United States will need 

to retain some of the tools and methods specialized 

for the purpose of differentiating friend from foe and 

combating networked adversaries. There is no hint that 

the threats from nonstate actors will soon subside, nor 

will the threat of “hybrid conflicts” where combatants 

fight without uniforms and conventional formations. 

The United States is also likely to conduct operations 

within environments characterized by weak or nonexistent identity regimes. Many of these challenges will 

continue to demand a place for the tools and methods 

of iWar.

With this reality in mind, the next section examines the emerging technology landscape and attempts 

to identify some of the general trends that are likely to 

shape how iWar will be waged in the next generation. 

Many of these tools have already demonstrated their 

utility on the battlefield and in securing the border, 

including biometrics, forensics, information management technology, and data analysis. These areas will 

continue to evolve and are likely to be enhanced by 

innovations in other areas, including new biometric modalities, improved DNA forensics, cyber tools 

and social media exploitation, advanced video and 

data analytics, machine learning, and computational 

analysis, among others.
57

This task of predicting technology futures is 

fraught with hazard. Estimations of what tools may 

emerge, how they will be used, and the implications 

of their use will inevitably appear naive in hindsight. 

Nevertheless, such speculation is a necessary risk for 

the strategist and planner who must allocate today’s 

resources based on reasoned expectations of what may 

happen tomorrow. For this reason, the next section of 

this study introduces several emerging technology areas through a hypothetical iWar scenario involving a 

suspected foreign fighter. While the details and events 

are fictionalized, the vignette represents a realistic security dilemma for the age of iWar and addresses the 

continuing challenge of identifying, screening and 

targeting individual adversaries determined to use 

anonymity to operational advantage. (See Chart 1.)

Industrial Warfare iWar

Political Context

Westphalian; professional armies fighting 

as political proxies for 

defined geopolitical 

objectives; recognizes 

jus in bello constructs.

Extra-Westphalian; 

individual combatants 

fighting for ideological 

causes and ambiguous 

objectives; challenges 

jus in bello constructs.

Adversary 

Characteristics

State armies comprised of “generic” 

professional soldiers 

using doctrinal organized formations and 

functioning by depersonalized, bureaucratic 

logic.

Nonstate entities and 

“unprivileged” combatants using anonymity 

for operational advantage; use idiosyncratic 

tactics and organized 

by highly personalized 

networks.

Chart 1. Industrial Warfare versus iWar.
58

Industrial Warfare iWar

Operational 

Environment

Contested primarily in 

the physical domains 

(land, sea, air) and 

waged in a contiguous 

linear battle space; 

defined by clear operational boundaries, 

fire and maneuver over 

geographic terrain.

Contested primarily 

in the informational 

domain (influence, 

identity, human terrain); spatially and 

temporally unbounded; 

defined by a merger of 

external and domestic 

security spheres of 

concern.

Theories of 

Warfighting

Influenced by tenets 

of maneuver warfare: 

mass, firepower, 

destruction of enemy 

forces, and seizure of 

key terrain. Focus is on 

the operational level 

of war.

Influenced by counterinsurgency and 

counterterrorism 

doctrines: populationcentric measures and 

law enforcement-like 

methods. Merges tactical and strategic levels 

of war.

Analytical Approach 

and Tools

Order of Battle analysis 

and indications and 

warnings (I&W) methods, platform-centric 

doctrinal templating, 

conventional Military 

Intel analytical models, 

technical signatures 

and intelligence, surveillance, and reconnaissance (ISR).

Social Network 

Analysis, Attack the 

Network, Identity 

Intelligence (I2), law 

enforcement analytical 

model, biometric and 

forensic signatures, 

document and media 

exploitation.

Targeting Paradigm

Status-based targeting 

against units, formations and equipment; 

Well-defined, rulesbased rules of engagement (ROE).

Identity-based targeting against individuals, 

cells, and networks; 

Ambiguous, evidencebased ROE.

Chart 1. Industrial Warfare versus iWar. (cont.)
59

Chart 1. Industrial Warfare versus iWar. (cont.)

THE FUTURE OF IWAR

Many of the catalysts giving rise to the iWar 

paradigm reflect persistent trends that are unlikely 

to abate in the immediate future. From a policy perspective, there is reason to expect some continuation 

of approaches based on the persistent monitoring of 

individual threats, identity-based screening, and, 

when necessary, personality-based targeting. The use 

of such methods remains relatively uncontroversial 

from a political perspective, and President Obama has 

made clear his vision of a counterterrorism strategy 

Industrial Warfare iWar

Objectives and 

Measures of 

Effectiveness 

(MOE)

Attrition and physical 

destruction of the adversary’s war-fighting 

capabilities; quantitative assessment - units 

destroyed and terrain 

seized. MOE based on 

technical Battle Damage Assessment.

Attrition of key leadership, nodes, and 

specialized operators; 

qualitative assessment—kill/capture of 

High-Value Individuals. 

MOE based on SNA 

theories of network 

centrality, influence, 

and organizational 

cohesion.

Success Criteria 

and End State

Primary focus on 

defeat of adversary 

military forces, political capitulation, and 

the orderly demobilization and repatriation of 

detained combatants.

Primary focus on risk 

mitigation rather than 

military victory; no 

political settlement, legal limbo for detained 

combatants, and 

enduring problems of 

fighter recidivism.
60

designed as a “surgical, precise response to a very 

specific problem.”150 Another important trend that is 

likely to remain constant is the value of anonymity as 

an operational enabler for many U.S. adversaries. Recent conflicts in Syria, Ukraine, and elsewhere have 

vividly highlighted this fact. As one commentator recently noted, in modern warfare, “Knowing the name 

of the person on the other-side of the battlefield is 

rising as a strategic necessity.”151

The next section examines in greater detail several 

of the factors likely to reinforce the central pillars of 

the iWar paradigm. These include characteristics of 

the threat environment, emerging technology trends, 

and the changing meaning of identity.

iWar and the Threat Environment.

Several persistent trends in the contemporary 

threat environment appear likely to amplify many of 

the original catalysts that gave rise to the iWar paradigm. Recently, General McMaster described the transition from a Cold War model where nations were 

the primary source of threats, in contrast to today’s 

threats: 

from nonstate actors and the confluence of networked 

insurgent and terrorist organizations bridging over 

into transnational organized crime networks and having access to capabilities they didn’t have in the past.152

These threats include an increased potential for hybrid 

conflicts where conventional status-based targeting 

methods may not easily apply, as well as a wide range 

of adversaries determined to leverage anonymity for 

operational advantage.
61

The National Intelligence Council Global Trends report recently described a near-future security environment dominated by various forms of irregular warfare—terrorism, subversion, sabotage, insurgency, 

and criminal activities.153 Testimony by the Director of 

the National Counterterrorism Center noted that these 

networks have actually grown more dangerous over 

time due to the erosion of central governance, making them “more geographically diffuse and involving 

a greater diversity of actors,” thus making them an 

increasingly adaptive and complex threat.154 Robert 

Cardillo, head of the National Geospatial-Intelligence 

Agency, recently observed that such groups and individuals are operating “literally without geographic 

bounds,” thus less vulnerable to conventional military 

targeting methods.155

There has also been an apparent shift in terrorism tactics away from infrequent but spectacular 

operations toward a pattern of smaller, more distributed attacks often emanating from self-radicalized 

individuals and small groups operating independent 

of centralized direction. The Global Trends report suggested that these: 

individuals and small groups will have greater access 

to lethal and disruptive technologies enabling them to 

perpetrate large-scale violence—a capability formerly 

the monopoly of states.156

The challenge of defending against such diverse 

threats is likely to reinforce the key aspects of iWar, 

particularly U.S. reliance on technical collection, identity-based screening, and targeting efforts focused 

against individual actors, small cells, and networks. 

This includes an immediate challenge arising from the 

issue of foreign fighters. 
62

The current Syrian situation exemplifies the enormous scale of the foreign fighter problem, exacerbated 

by relatively easy cross-border mobility and aggressive 

social media recruiting. According to recent estimates, 

as many as 20,000 foreign fighters from some 90 countries have joined various factions fighting in Syria, including thousands of European passport holders and 

several hundred from the United States. The head of 

the National Counterterrorism Center recently called 

the foreign fighter flow to Syria “unprecedented,” at a 

rate far exceeding the movement of foreigners traveling to Afghanistan, Pakistan, Iraq, Yemen, or Somalia 

at any point over the past 20 years.157

Outside the United States, most countries have instituted little more than piecemeal strategies for monitoring and tracking foreign fighter flow down to the 

level of the individual combatant. For example, the 

European Union (EU) still does not have a systemic 

process in place for identity checks of all citizens reentering the region from abroad. During 2013 alone, 

Turkey denied entry to some 4,000 people listed in a 

no-entry database and detained another 92,000 people 

on its border; however, this has done little to stem the 

tide.158 One recent United Nations (UN) report noted 

that the “slow exchange of information and data on 

foreign fighters is one of the major obstacles to international counterterrorism cooperation,” estimating 

that “less than 10 percent of basic identifying information on such individuals has been entered into global 

multilateral databases.”159 That means that there is 

little, if any, timely operational information available 

concerning identity, travel routes, photographs, and 

international search warrants on individuals traveling to take part in hostilities. There has been a similar 

challenge with obtaining physical evidence of foreign 
63

fighter activities occurring while inside the conflict 

zone, making it difficult to pursue prosecution as an 

option or generate additional intelligence leads.160

One U.S. senior intelligence official working on the 

Syrian foreign fighter problem recently described the 

challenge as a “decadal” issue of concern for the intelligence community.161

In the aftermath of the January 2015 Paris attacks, 

it became clear that European intelligence agencies 

had far too many leads to follow, too many returning foreign fighters to monitor, and not enough manpower for all of the required tasks.162 Unfortunately, 

few, if any, countries possess the resources required 

for monitoring large numbers of suspect individuals 

through traditional surveillance methods. Some estimates place the cost of full-scale, round-the-clock, 

conventional surveillance against a single individual 

as high as $8 million a year.163 Even a fraction of this 

cost represents an unsustainable outlay set against 

potentially thousands of foreign fighters expected to 

return home eventually from conflicts such as in Syria. 

This reality inevitably will lead western governments to rely upon some combination of two approaches. First, they will expand the use of technical 

surveillance and screening measures aimed at gathering more information with fewer resources. Second, 

they will develop improved methods of risk analysis 

and perhaps apply predictive interventions against 

suspect individuals. Either of these approaches will 

tend to reinforce the key elements of the iWar paradigm 

as security threats must still be identified, monitored, 

and targeted with timely and accurate information. A 

variation of the iWar paradigm has already been proposed in France where lawmakers recently approved 

an unprecedented overhaul of domestic surveillance 
64

practices, now allowing for collection and analysis 

of telephone and internet metadata as one means 

of identifying potential threats posed by domestic 

extremists.164

Beyond threats from international terrorism, many 

security analysts have highlighted the growing risk of 

“hybrid conflicts” where state-based militaries attempt 

to obscure operational attribution by employing proxies and irregular tactics, such as with Russia’s use of 

“little green men” in Ukraine.165 A recent report by the 

International Institute for Strategic Studies highlighted the possibility of nonstate actors also employing 

such mixed tactics to achieve asymmetric advantage 

against conventional adversaries.166 The Islamic State 

has demonstrated such a hybrid approach by employing a high-low mixture of conventional maneuver, insurgent-like tactics, and terrorism to achieve gains in 

Syria and Iraq. French forces in Mali have experienced 

similar challenges in waging a conventional military 

campaign against an “invisible enemy” where it has 

been difficult to “distinguish between the trafficker, 

the terrorist, and the rebel.”167 Even future challenges from peer competitor state militaries are likely to 

circumvent the significant U.S. advantage in conventional warfare by employing techniques that avoid 

direct confrontation and veil operational attribution. 

One commentator on defense technology recently 

observed that “war is changing, whether it is waged 

by emergent groups like the Islamic State or nations 

like Russia, more and more, the potential revelation 

of identity is becoming a liability in conflict zones.”168

The expanse of U.S. global security interests also 

suggests continuing involvement in activities other 

than high-intensity conventional warfare, such as stability operations, anti-piracy, and counterillicit traf-
65

ficking.169 These tasks share several common characteristics. First, they represent activities dominated by 

networked adversaries, disbursed cells, and individual actors using anonymity for operational advantage. 

Second, such missions are likely to occur in areas characterized by weak governance and ineffective identity 

regimes where a significant portion of the population 

lacks formal documentation and verifiable identity. 

Such environments are notorious generators of corruption, insecurity, and instability—issues that cannot 

easily be contained through strategies of conventional 

military deterrence.

The challenge of conducting operations under 

weak identity regimes is generally underappreciated 

as a military planning factor. This may be due, in part, 

to the fact that many residents of the developed world 

take for granted the basic identity management functions of the modern state bureaucracy. This structure 

begins with foundational identity documents such as 

birth certificates then expands through one’s lifetime 

within a system of de facto credentialing, sanctioned 

by the state, that are required for full participation in 

civic and economic life. Yet, even this most basic function of governance is absent in much of the world. According to one recent estimate, as many as 40 percent 

of children in the developing world do not have any 

kind of official identity registration generated at birth. 

This situation creates an initial “identity gap” that 

becomes prone to inaccuracies, corruption, and disenfranchisement throughout an individual’s lifetime.170

The operational significance of identity is not only 

a concern for counterterrorism and insurgency, but 

also in peacekeeping and humanitarian relief missions. In 2014, the UN High Commission for Refugees 

(UNHCR) launched its first biometric pilot program 
66

in Malawi to improve security protections and better 

target assistance for forcibly displaced persons.171 This 

program was recently expanded to the Democratic 

Republic of Congo in an effort to identify and register 

some 245,000 Rwandan refugees living in the region 

and 19 refugee camps in Chad where 450,000 individuals will be biometrically enrolled. UNHCR will 

update records using remote connections to a central 

biometric database maintained in Geneva, Switzerland.172 In another example, the Turkish Disaster and 

Emergency Management Authority recently conducted biometric registration of 740,000 Syrian refugees 

displaced by fighting across the border.173 Such identity management tools are likely to become the norm 

in future relief operations and humanitarian interventions in these environments.

iWar and the Technology Environment.

The changing technology environment is another 

factor that is likely to transform significantly the relationship between security, identity, and warfare. 

National security scholar Rosa Brooks recently posed 

the question of how warfare will be changed in an 

age when we increasingly know our enemy by name, 

by face, and even DNA. She observes that, in recent 

conflicts, the United States often knows a great deal 

about their adversaries, “between human informants 

and high-tech surveillance, we often know where our 

targets were born, where they went to school and college, the names of their siblings and children, their favorite pastimes, and much more.”174 Brooks and others 

like military-legal scholar Charles Dunlap have even 

speculated on the not-too-distant future where ultraprecise weapons armed with advanced facial recogni-
67

tion software will “roam battlefields looking for very 

specific members of an enemy’s force.”175 While this 

scenario may not yet be reality, several information 

technology trends are already pushing iWar in this 

direction.

The next decade is likely to be remembered as the 

era when biometric technologies became normalized 

and integrated into nearly every facet of daily life. The 

defense, security, and law enforcement sectors were 

the first to recognize the value of biometrics for applications such as border control, systems access, and 

identity verification. These requirements largely defined the initial focus of research and development in 

the field, as well as how these tools evolved; however, 

this dynamic is rapidly changing. Private industry is 

already racing ahead of governments in the use of biometric technologies, particularly in the areas of personal consumer electronics and information security 

tools.176 The implication is that governments increasingly are likely to trail behind in terms of influencing 

the direction of innovation. Industry experts have expressed concerns that a lack of proper planning and 

coordination among governmental agencies, and between international partners, will further diminish the 

role that governments play in defining the future of 

the industry.177 New technical breakthroughs are likely to emerge from demand in the commercial sector, 

only later to be exploited by defense and security interests. In some cases, this reactive position will apply 

to matters of governance and rule setting, particularly 

as technology innovation outpaces the state’s ability 

to define the context of its use.

This situation is a dramatic reversal of the Cold 

War era paradigm in which the most advanced sensors and intelligence collection platforms evolved 
68

behind the wall of protected government-sponsored 

research and development. This has several important 

implications. First, in some technical areas of iWar, 

the United States will find itself in a situation of technological parity with potential adversaries, whether 

state actors or otherwise. Second, in order to reduce 

the technology gap, the U.S. Government will need to 

resolve many long-standing deficiencies in the acquisition process in order to remain near the leading edge 

as new technologies evolve. Biometrics is one area in 

particular where this dynamic is almost certain to be 

the case.

One industry research organization predicts that, 

by the end of 2015, there will be some 619 million people using biometrics on their mobile devices, including 

fingerprint, voice, iris, facial, and various modalities 

of behavioral biometrics.178 Other estimates suggest 

that over a billion people in developing countries have 

already registered some form of biometric signature 

through their interface with consumer electronic devices, financial institutions, and local governments.179

Additionally, the inherent weaknesses of traditional 

methods for personal authentication (e.g., passwords) 

are driving significant growth from the commercial 

sector for improved methods of identity management.

Beyond these commercial applications, dozens of 

governments are pursuing biometrically enabled identification (ID) projects for e-passports and visas, voter 

registries, public sector payroll, health care, and social 

service delivery programs. Many of these initiatives 

are originating in the developing world where some 

governments have entirely bypassed a paper-based 

identity system and moved directly to biometrically 

enabled technologies. Notably, India, with a massive 

undocumented population, is currently in a multiyear 
69

process of biometrically registering all one billion residents as part of a new national identification system. 

Biometric attributes will be linked to a unique identity 

number and used for such functions as managing government benefits and monitoring distribution of food 

rations.180

As another example, the United Kingdom (UK) 

Home Office recently introduced a “biometric residence permit” that will be required for any foreign 

nationals from outside the European Economic Area 

who wish to work or study in the UK. The cardholder 

must present these credentials at the border when 

travelling into or out of the UK.181 Other novel applications are emerging each day such as Kenya’s use of 

biometric cards to ensure public servants show up to 

work and Nigeria’s recent legislation mandating biometric registration of mobile phone Subscriber Identity Module (SIM) cards so the government can better 

track illicit activities.182 Pakistan recently followed suit 

in requiring biometrically registered SIM cards after 

it was discovered that all six assailants in the major 

December 2014 attack on a Peshawar military school 

were using cell phones registered to a single woman 

with no obvious connection to the attackers.183

The rapid pace of innovation in the field is certain 

to raise concerns over privacy and surveillance. Additionally, there are likely to be challenging issues with 

regulatory and governance concerns as well as conflicts over technical standards, interoperability, and 

data integration. Perhaps most significantly, much of 

the development in this area is likely to evolve outside 

of explicit state control. Governments, security services, and militaries may find themselves in the position 

of being rule-takers rather than rule-makers when it 

comes to defining how this technology proliferates 
70

and is used by the general public. Biometrics is just 

one example of this general trend concerning technology innovation in the age of iWar. A similar dynamic 

applies to the cyber domain as well.

In recent conflicts anonymity has become an increasingly important center of gravity for nonstate 

actors, and, in some cases, for state actors attempting 

to avoid operational attribution. Protected identities 

can provide these adversaries with flexibility, mobility, and the advantage of surprise. Anonymity also allows for safety in communication, recruitment, financing, and planning, as well as a form of concealment 

from which to wage information warfare. The cyber 

domain offers adversaries a zone of relative sanctuary where real identities can be protected, concealed, 

or manipulated. Actors can conceal activities behind 

strong encryption, digital avatars, or technical tools 

that complicate attribution and thwart surveillance.

DoD’s recently updated cyber strategy explicitly 

noted how anonymity on the web is directly enabling 

malicious cyber activity by state and nonstate groups. 

The Islamic States in Iraq and Syria (ISIS) has been one 

of the more notable examples of this trend. Another 

has been Russia’s use of Internet “trolls” for generating pro-regime propaganda aimed at influencing 

the information environment concerning operations 

in the Ukraine. One recent study detailed how these 

anonymous “trolls” are expected to post 50 news 

articles daily, maintain multiple Facebook and Twitter accounts, and generate dozens of posts per day, 

amounting to some 40,000 online comments each day 

in support of pro-Kremlin information operations.184

In recognition of these new challenges, the DoD Cyber Strategy specifically calls for “intelligence and attribution capabilities help to unmask an actor’s cyber 
71

persona, identify the attack’s point of origin, and determine tactics, techniques, and procedures” in order 

to support credible deterrence, response, and denial 

operations.185

Estonian President Toomas Hendrik Ilves has been 

a leading thinker on cyber defense issues and recently 

noted how organized crime, terrorist networks, and 

state actors are rapidly converging into this space, 

making it increasingly difficult for legitimate actors to 

distinguish between them.186 The challenge of identity 

and attribution in the cyber domain has made it a virtual battleground for “phase zero” operations, those 

activities designed to shape perceptions and influence 

behavior as a precursor to kinetic action. Recent examples were seen prior to Russian military interventions in both Georgia and Ukraine. More broadly, 

the cyber domain offers a venue for influencing behavior and shaping outcomes through activities short 

of conventional military engagement. This approach 

entails fewer physical risks for adversaries by offering a greater ability to mask identities through readily available technical tools. These characteristics are 

making cyberspace a highly contested terrain in the 

age of iWar. Many of the enabling technologies in this 

domain are democratic and highly scalable, meaning 

that, unlike the most powerful weapons of the Cold 

War era, cyberspace weapons are generally accessible 

to anyone with the requisite skills and basic tools. For 

this reason, cyber presents the logical platform for 

nonstate actors seeking to employ asymmetric capabilities against states while avoiding direct attribution.

A variety of easily accessible tools can offer users 

parallel networks that are encrypted, decentralized, 

and, to some degree, anonymous. Silk Road, an online “dark web” trading site recently shut down by the 
72

FBI is one example. Other actors rely on tools, such as 

the Tor network, that offer protections against traffic 

analysis and surveillance by masking Internet protocol (IP) addresses and server locations while encrypting data packets and routing them through multiple 

nodes, making it difficult to track and identify users. 

Some experts estimate that such dark web sites represent a significant portion of web activity, involving 

content generally not indexed and undiscoverable by 

conventional search engines such as Google.187 In addition, digital “crypto-currencies” such as Bitcoin can 

provide a means for conducting pseudonymous financial transactions across these networks.188 This architecture of anonymity enables users to browse, communicate, and trade illicit materials with a generally 

lower risk of detection and greater ability to conceal 

physical locations and identity.189

There is already some suggestion that the Islamic State and other extremist groups are using such 

methods. The head of the U.S. Treasury Department’s 

Financial Crimes Enforcement Network recently observed that, “when you start talking about global 

point-to-point transactions and pseudonymity and instantaneous movement of value over borders, that has 

real risks associated with it.”190 Director of the NSA 

Admiral Michael Rogers more clearly articulated these 

risks, noting that as his agency monitors threats on the 

dark web it spends “a lot of time tracking people that 

can’t be found.”191

The use of the anonymous “dark web” sites is 

particularly striking in contrast to the concurrent rise 

of social media, a phenomena conceptually based 

on the opposite goal—specifically the projection of 

identity for the explicit purpose of building network 

connections based on attributional affiliation. Par-
73

ticularly since the Arab Spring protests, social media 

has played a central role in how nonstate actors craft 

and project their identities outward to a global audience. For many groups, social media platforms have 

become the preferred tool for distributed communication, planning, recruitment, and the dissemination 

of propaganda, including terrorist groups, insurgents, 

and criminal organizations. The National Intelligence 

Council Global Trends report recently described these 

social networking technologies as “inherently resistant to centralized oversight and control,” with the 

potential to displace traditional power sources and 

authorities in favor of individuals, small groups, and 

ad hoc coalitions of nonstate actors.192

Several recent examples demonstrate the degree 

to which social media has evolved as an operational 

tool. Militant groups in Gaza, terrorist cells in Mali, 

oil traffickers in Nigeria, and pirates off the Somalia 

coast have all employed social media as ad hoc command and control networks for conducting information operations. The Islamic State, in particular, has 

crafted a robust and sophisticated digital presence, 

“disseminating timely, high-quality media content 

across multiple platforms.”193 A recent report on terrorism and new media found that some 90 percent of 

terrorist activities taking place online use social media 

as a networking tool, in some cases offering “a virtual 

firewall to help safeguard the identities of those who 

participate.”194 The director of Great Britain’s NSA 

counterpart, the Government Communications Headquarters (GCHQ), recently described Twitter, Facebook, and WhatsApp as the “command-and-control 

networks of choice for terrorists and criminals.”195 One 

commentator on military technology even suggested 

that situational awareness in the cyber age will soon 
74

mean using “instantaneous data from social networks 

like Twitter and Facebook to identify the target in the 

sniper scope.”196 While perhaps an exaggeration, the 

comment does suggest the degree to which these platforms have become an important component of the 

contemporary operational environment.

Perhaps the Syrian conflict provides the most powerful example of how social media has become a tool of 

modern warfare. This conflict has been called “the most 

socially mediated civil conflict in history,” with fighters using Facebook, YouTube, Twitter, Diaspora, and 

Snapchat for a variety of operational,communication,

and propaganda functions.197 For example, the AlNusra Front has used social media for posting press 

releases and informal communiqués including text, 

photographs, and videos detailing the fighting, and 

even personalized eulogies for its members killed in 

combat.198 Analysis from late-2014 identified at least 

46,000 Twitter accounts being used by members and 

supporters of the Islamic State, while the FBI estimates that some 200,000 people around the world are 

accessing “terrorist messaging” each day via social 

media, videos, instruction manuals, and other material posted on militant Islamist sites.199

These platforms have also been used extensively 

for dissemination of operational information, recruiting, and training purposes.200 For example, sites have 

posted information on use of explosives, fighting techniques, and links to encryption programs designed 

to help users protect sensitive communications. Individual fighters are also using personal accounts to 

establish their bona fides and document their combat 

experiences first hand. In a telling example, one young 

British fighter attempted to fake his own battlefield 

death on social media after being placed on a travel 
75

watch list.201 Eventually he was caught when attempting to enter back into the UK; however, the example 

highlights the degree to which many nonstate actors 

are leveraging social media to craft and project identities to an external audience.

However, online activism can be a double-edged 

sword, both enabling adversary operations, while at 

the same time potentially exposing them to observation and attribution. In recent years, social media 

exploitation and open source digital forensics have 

offered invaluable insights into adversary operations. 

For example, in early-2014 analysts were able to track 

Russian military personnel movements into Crimea 

through social media “bread crumbs” dropped by 

personnel preparing for mobilization. Separately, 

YouTube videos and Twitter messages posted by 

Russian irregulars provided the first hints of responsibility for the downing of Malaysia Airlines Flight 

17 in eastern Ukraine in July 2014.202 More recently, 

“crowd-sourced” information posted to social media 

by local residents has substantiated Russia’s active 

role in eastern Ukraine, including the steady flow of 

weapons and troops, even as the government continues to deny any direct involvement in hostilities.203

In Syria, pro-Assad forces reportedly used fake online avatars to identify and target opposition members. 

They used these fictitious identities to obtain detailed 

personal information, including names and locations 

of opposition members, media activists, humanitarian aid workers, and others deemed dangerous to the 

regime.204 These Skype accounts, mobile applications 

(apps), and social media sites revealed address books, 

records of short message service messages, email, and 

other information relating to opposition force campaign plans. According to one assessment, this type 
76

of aggressive social media exploitation has produced 

“actionable military intelligence for an immediate battlefield advantage,” including information enabling 

pro-Assad forces to identify, track, and target key opposition members.205

The United States reportedly has also made use of 

social media data for targeting. One military official 

recently revealed that social media posts recently had 

been used to track and subsequently strike an Islamic 

State group headquarters building.206 Other researchers have used social media as a means of building 

demographic profiles of Islamic State fighters and 

other groups, shedding light on their locations, what 

languages they speak, how they access the Internet, 

and for content analysis of their communications.207

In another twist, the online international network of 

“hacktivist entities,” known as Anonymous, recently 

took control of dozens of Twitter and Facebook accounts used by the Islamic State and threatened the 

group’s members, promising “we will hunt you, take 

down your sites, accounts, emails, and expose you. . . . 

From now on, no safe place for you online.”208 According to one expert, such “network exploitation is now a 

routine part of even the most low-tech, if brutal, civil 

wars, and available to those operating on a shoestring 

budget.”209

The information density of the cyber domain clearly presents liabilities for the “non-OPSEC [operations 

security] aware” foreign fighter community as posts 

and communications provide a window into identities 

and activities.210 The design of social media networks 

is based on contact chaining and associational linkages between individuals. This information is often 

enhanced by content-rich data, including facial images, text, video, and audio, frequently with geolocation 
77

tagging and time stamps. Certain behavioral biometric signatures may also be derived from these routine 

digital activities showing patterns in how users post 

information and use their devices. Experts in the field 

claim that much of what seems to be anonymous 

digital presence is relatively easy to de-anonymize by 

correlating publicly available databases, credit card 

information, voter records, personal associations, and 

behavioral patterns—enough information to compile 

highly individualized dossiers on most general internet users. Such exposure provides a powerful tool for 

diagramming a network’s composition and identifying the individuals operating within them, calling to 

mind Johann Goethe’s maxim, “Tell me with whom 

you consort and I will tell you who you are.”211

Legal scholar and retired general Charles Dunlap has suggested that the expanding availability of 

“big data” and new cyber tools may soon enable “the 

hyper-personalization of war,” leading to the overt 

targeting of specific individuals both on and off the 

battlefield. Dunlap notes a “nearly endless number of 

scenarios where adversaries could hyper-personalize 

conflict via cyber means.”212 This future mode of warfare may already be taking place, exemplified by the 

recent penetration of computer systems at the Office 

of Personnel Management, allegedly by Chinese hackers associated with the People’s Liberation Army’s 

Third Department, a group charged with militaryfocused cyber-intelligence gathering. These hackers 

focused specifically on the personal data, work and 

medical histories of thousands of government workers, military and intelligence personnel, and defense 

contractors, including information on friends, family members, and associates derived from security 

clearance forms.213
78

One interesting facet that has evolved from the 

increased availability of open source data and social 

media content has been the democratization of intelligence. During the Cold War era, the use of advanced 

technical collection platforms was limited to a handful of major military powers. However, in the age of 

iWar, small groups, commercial entities, and even 

individual actors now have the ability to gather information independently and use it for a variety of 

purposes. Retailers routinely gather detailed information about spending habits, credit histories, websurfing histories, social network postings, and demographic information for market research and precisely 

targeted advertising. 

Similar methods have been used by free-lance 

intelligence analysts, impressively demonstrated in 

2013 when a blogger from England used social media 

sources to substantiate reports of the Syrian government using chemical-weapons against opposition targets in a Damascus suburb. The same individual later 

confirmed reports that the Free Syrian Army had obtained anti-aircraft guns, then was able to closely track 

the tactical progress of Syrian rebels and detail their 

development of improvised weapons.214 Another recent example involved “crowd sourced” intelligence 

derived from a geotagged social media post containing 

photos of Russian military vehicles moving through 

Ukraine.215 This growing database, developed entirely 

from user-generated reports aggregated on a commercial platform, has become a virtual catalogue of 

incursions by Russian troops, equipment, and ceasefire violations.

Despite the clear value of such tools, there are significant challenges in using much of the content obtained from social media and user-generated content. 
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From an analytical perspective, there is the difficult 

task of filtering potentially useful data from the massive amount of digital clutter, noise, and misinformation. A second issue is one of attribution, specifically 

knowing the identity of the individual behind the 

information. As the classic New Yorker cartoon prophetically observed in 1993, “On the internet, nobody 

knows you’re a dog.” Unlike with biometrics, online 

digital identities are created, not inherited. These are 

sometime referred to as “cyber personas” and represent how a user is identified to the larger network 

through digital attributes such as email address, computer IP address, or cell phone number.216 However, 

these identities can be engineered, concealed, and 

manipulated for specific purposes.

Most savvy social media users practice at least 

some forms of operational security, while many others intentionally operate behind veils of fictitious 

identity and deception. Cyberspace is rife with fake 

twitter accounts, digital avatars, and using anonymizing software that conceals identity. Jihadist forums 

now advise participants on sophisticated measures 

for avoiding detection when browsing, including 

steps for removing geolocation and metadata from 

cell phone images.217 Increasingly, OPSEC-savvy militants are also using encrypted messaging apps such 

as WhatsApp and Kik, or data-destroying apps such 

as Wickr and Surespot that make it very difficult for 

legitimate authorities to identify actors and track their 

communications. The vast amount of deception and 

misinformation in the cyber domain has spawned a 

cottage industry of firms specializing in verifying social media accounts using information such as geotracking, metadata, speech, and content analysis to 

identify imposters and fictitious identities.218
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The challenge for legitimate operators is in linking 

the cyber persona to the individual behind the digital representation. As one cryptographer and security 

expert recently noted, “We’re living in a world where 

we can’t easily tell the difference between a couple of 

guys in a basement apartment and the North Korean 

government,” in effect creating “an arms race between 

attackers and those that want to identify them.”219 In 

response to such challenges, the CIA reportedly is 

undertaking a broad restructuring based in part on 

the realization that this new environment has fundamentally changed how espionage is conducted. As 

part of this new approach, the organization reportedly will increase cyber capabilities across every category of operations, for example, using cyber tools for 

“confirming the identities of targets of drone strikes 

or penetrating Internet-savvy adversaries such as the 

Islamic State.”220

All of these issues link back to the foundational 

components of the iWar paradigm, individualization, 

identity, and information. As identity-based screening and personalized targeting become normalized, 

the issue of identity and attribution will only grow as 

an operational concern. For this reason, it is important 

also to consider how the concept of identity itself may 

change in the future.

iWar and the Future of Identity.

The concept of identity can be frustratingly abstract, particularly as compared to more concrete aspects of national security strategy and warfare. Arguably, the very meaning of identity is in the midst of a 

revolutionary transformation, with changes that undoubtedly will have major implications on how iWar 
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is waged in the future. This section briefly considers 

some of these factors and offers speculations on the 

social, economic, and technical trends likely to shape 

the meaning of identity as it relates to future national 

security challenges.

As this monograph has posited, identity within the 

iWar paradigm has become an operational “center of 

gravity” for networked adversaries, nonstate actors, 

and individual combatants. As Carl von Clausewitz 

conceptualized the term, it represents “the hub of 

all power and movement, on which everything depends.”221 In conventional conflicts, this generally refers to an adversary’s military capabilities, those assets 

and qualities enabling it to apply combat power on 

the battlefield. In the case of irregular warfare and insurgencies, the center of gravity generally is viewed in 

terms of political legitimacy, security factors, and the 

ability to exert influence over a population. At its essence, it represents an adversary’s source of strength, 

as well as its greatest weakness and vulnerability.222 In 

the age of iWar, the new center of gravity has become 

identity.

The cohesiveness of a network comes from identity and the associational relationships among its 

members. Identity is what distinguishes social networks from bureaucracies. Whereas bureaucracies 

are depersonalized and operate on the basis of procedure and protocol, social networks derive strength 

from interpersonal connections, bonds of trust, and 

expectations that are perceived rather than codified. 

The integrity of an adversary network depends upon 

the confidence that identities can be internally trusted 

and externally protected. Denying anonymity to an 

adversary’s network deprives it of its unique power. 

As actors are identified, connections are exposed, and 
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activities are attributed, the network becomes vulnerable. With this exposure, it loses the ability to operate, 

to maneuver, to plan, and to communicate. 

As the glue of network cohesion, it is important to 

understand how the concept of identity is changing. 

Many of these transformations are due to recent technical innovations that are changing how identity information is created, stored, exchanged, and verified. 

Additionally, social factors are eroding the state’s traditional function as the sole authority for ascribing official identity. Individuals as well as corporations are 

taking the lead in defining new identity concepts and 

creating the context for how they are used. Economic 

forces are also playing a role, such as the personalization of commerce and growth of the “sharing economy.” These changes are introducing new mechanisms 

for identity verification, transactional attribution, and 

the increasing importance placed on reputational interest. As a result, new forms of digital identity are 

evolving in order to verify who is participating in 

these exchanges and whether or not these agents can 

be trusted.

These trends suggest the beginnings of a new system of identity governance. In this environment, individuals and commercial interests will challenge state 

authority in the mediation of identity. Governments 

will likely retain some powers to define the terms 

of official identity by means of controlling access to 

certain privileges and benefits; however, individuals 

will increasingly have the capability to control, negotiate, and manipulate their own identities, particularly 

within the digital environment. Commercial interests 

will emerge as for-hire facilitators helping to manage 

these personal identities, either as an explicit customer 

service or for the purpose of commoditizing identity 

information for use by third parties.
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These changes reflect a paradox of how identity will 

evolve in society. On one hand, these technologies are 

enabling ever-greater personalization of experience. 

This is seen in the expanding use of social media, the 

individualization of education and medicine, technologies for continuous bio-monitoring, and mobile apps 

optimized for individual habits, personal preferences, 

and daily patterns of life. On the other, countervailing forces are pushing for improved technical options 

to protect privacy and personal information, and to 

ensure anonymity in the digital environment. This is 

reflected in the expanding use of darknets, strong encryption, and crypto-currencies providing individuals 

with the ability to communicate securely and conduct 

their transactions free from surveillance. 

Public ambivalence over privacy and personal security issues generally reflect this tension. According 

to a recent Pew survey, some 93 percent of U.S. adults 

consider it important to be in control of who can get 

information about them; however, far fewer (63 percent) feel it is important to be able to “go around in 

public without always being identified.”223 Yet, survey 

results also suggest that most Americans are somewhat resigned to the fact that much of their digital 

activity does not remain private and secure, and even 

fewer feel that they have much power to control how 

this data is used.224 Furthermore, even as Americans 

profess a desire for digital anonymity, very few take 

proactive technical measures to enhance privacy or 

protect online activity from surveillance. These conflicting attitudes are likely to remain in tension and 

will play a significant role in shaping the future of 

identity, including its relationship to national security. 

Perhaps most importantly, the rapid pace of technical 

change likely means that government will be in a reac-
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tive rather than proactive role shaping how this future 

identity concept evolves.

One change that is likely to have major implications 

for individuals as well as governments is the idea of 

identity merger. Today, identity information remains 

somewhat heterogeneous in terms of its format and 

how it is stored and used. This traditional identity 

construct is like a two-dimensional mosaic formed 

from a plurality of nonoverlapping, distinctly formatted bits of information. This is represented through 

a combination of sources including state-sanctioned 

identity documents (passports and birth certificates), 

unofficial biographic information (newspaper articles, 

yearbooks, and club memberships), bio-informational 

data (medical records, prescriptions, and insurance 

documents), and digital media (social networking, 

email, and phone records). This legacy construct contains an enormous amount of identity information; 

however, much of this data is highly fragmented and 

perishable (in physical form and paper records), or 

maintained in formats making the information difficult to discover, correlate, and analyze. Additionally, 

these traditional identity markers generally are based 

on explicit content and declared associations such as 

family names, phone numbers, physical address, employer, social security numbers, etc.

The characteristics of a new identity construct are 

likely to be very different from the existing paradigm. 

It will be less of a mosaic and more of a homogeneous 

composite that is defined by deeply embedded relationships derived from analytical correlations rather 

than explicit information. These implicit identities 

will not be defined by format and will blur the existing distinctions between biographic, biometric, behavioral, and digital information. These composite 
85

identities will be persistent, cumulative, and associative—meaning that they will be based on information 

that endures forever in digital archives, is constantly 

accumulating as new data points, and contextually enriched through deep correlation to other people, places, and activities. These new identities will not exist as 

discrete nodes of data, but rather will be defined in the 

context of their relationship to other things. Thus, this 

future identity concept will be one that is inherently 

networked, temporally deep, and multidimensional 

in ways that current identities are not.

A hint of this future is already apparent in how 

individuals are using smart phone technologies and 

other networked devices. Many of these are already 

enabled with multi-modal biometric authentication 

(fingerprint, iris, facial, and keystroke/gesture recognition). This means that any activity associated with 

a particular device can be unambiguously linked to 

other unique identity signatures such as voice and 

data transmissions, behavioral patterns, geospatial 

movements, and social network activity. One implication of this identity merger will be the potential for 

more powerful means of passive monitoring and surveillance. As digital identities increasingly are merged 

with other biometric data, behavioral patterns, and 

contextual biographic information, acts of intentional 

deception may also become easier to detect. Under 

Secretary of Defense for Intelligence Mike Vickers 

recently observed how the tracking of individuals 

around the globe has become easier in recent years 

due to “the digital dust that we all leave around as we 

lead our lives.”225

Google is already demonstrating some practical 

examples of this changing identity concept with its 

Project Abacus, a real-time security system using a 
86

combination of facial recognition, voice detection, and 

behavioral data to enable continuous device authentication without the use of traditional passwords.226 The 

next phase of iWar is likely to focus on such nontraditional signatures and sensors able to detect and resolve 

a wide range of discrete and ambiguous identifiers. 

This will be combined with new analytical tools able 

to discover and correlate these attributes from massive data streams of heterogeneous and fragmented 

information. The final section of this monograph will 

examine several of these emerging technology areas, 

discuss the potential application of these tools, and 

consider how they may shape how iWar is waged in 

the future.

TECHNOLOGY FUTURES: WAGING 

IWAR IN THE NEXT GENERATION

The following scenario and technology discussion 

addresses three interrelated tasks that are central to 

iWar: Identity Discovery, Operational Attribution, 

and Mapping the Network. In describing these tasks, 

this section explores a number of emerging technology areas just beyond the operational horizon. While 

intended to be realistic, it should be noted that this scenario presupposes certain organizational capabilities, 

operational methods, and information sharing policies not yet in existence. Nevertheless, the hypothetical case study provides a context for examining how 

a range of new technical tools could soon be applied 

for operational use and shape how iWar is waged in 

the future.
87

Task One: Identity Discovery.

A disheveled young man speaking in accented English 

enters Istanbul’s Ataturk Airport with a one-way ticket to Amsterdam. His shoes are covered in mud, shirt 

slightly tattered, and his appearance suggests several 

days without bathing. As he enters the immigration 

control area, the man is visibly apprehensive as he approaches the customs agent. He has no checked luggage and only a small carry-on bag containing a cell 

phone, USB drive, travel documents, and a few personal items. As the agent scans his boarding card, the 

Passenger Name Record data appears on the screen 

giving the name, address and a credit card number 

associated with the ticket purchase. The agent then 

notices an alert message indicating that the passport 

number was recently listed in INTERPOL’s Stolen and 

Lost Travel Document database. On closer examination, the agent notices that the passport picture appears somewhat different from the man’s appearance. 

Based on these concerns, the suspect is pulled aside 

for secondary screening and not permitted to board 

the flight.

The first challenge of this scenario is resolving the 

identity of a suspicious individual potentially traveling under false documentation. Several standard 

biometric modalities are currently in use for establishing identity and screening individuals against watch 

list information. These include the most commonly 

used biometric modalities of facial, fingerprint, and 

iris. While facial recognition remains somewhat less 

reliable than other biometric modalities for identity 

verification, it has advantages over other modalities 

that typically require subject cooperation and direct 

contact. Of the standard modalities, facial recognition 

has seen perhaps the most change in recent years due 

in part to a rapidly expanding range of commercial 

applications. 
88

Recent performance evaluations of facial recognition algorithms conducted by the National Institute of 

Standards and Technology reported that overall accuracy in the field has improved up to 30 percent since 

2010.227 For example, improved algorithms have enhanced applications for detecting fraudulent passport 

and driver license applications, methods for access 

control and area surveillance, and digital forensics 

in criminal investigations. Among commercial apps, 

Facebook has accumulated what experts estimate to 

be the largest facial recognition database in existence, 

used for identifying and tagging individuals appearing in user-generated photos.228 According to the company’s research team, its face matching software has 

achieved over 97 percent accuracy for matching faces 

between two unfamiliar photos, performance levels 

roughly equivalent to human capability. This includes 

photos taken under variable lighting conditions and 

even in some cases where the subject is not facing 

directly into the camera.229

Conventional facial recognition apps generally 

work by applying algorithms to extract features from 

images using one of two approaches. The first is geometric, or feature based, which decomposes the facial 

structure into components known as eigenfaces. These 

components are then compared with other images by 

measuring distance between respective features. A 

second approach, referred to as photometric, or view 

based, uses a statistical approach for distilling an image into values and comparing them with known 

templates.230 With either method, the major technical 

challenges include dealing with low-quality images, 

changes in subject appearance, and pose variations.231

Additionally, there are a number of technical limitations in terms of capturing usable imagery at long 
89

distances, during nighttime, and from low-resolution 

images of the kind obtained from webcams, ATM 

cameras, or surveillance video.232

Facial identification based on a combination of 3D 

data and infrared images has been demonstrated as one 

potential method for enhancing current approaches.233

This can be accomplished by converting 2D images to 

3D avatars, using mathematical modeling in order to 

capture data lost in standard images. Additionally, 

3D image scanning combined with thermal modeling 

offers a means of potentially overcoming challenges 

with pose variation and lighting conditions that can 

reduce accuracy. These methods are also technically 

more difficult to spoof than standard approaches. 

Current 3D scanning technologies are better suited 

to access control rather than surveillance; however, 

other developments may soon offer expanded utility. 

Some of these include the use of face-texture analysis, 

improved algorithms for image extraction from infrared video, as well as methods for using composite 

images derived from multiple video frames.234 Other 

approaches involve the use of 3D topology and cameras that are able to capture data outside the visible 

spectrum such as near-infrared and long-wave infrared thermal. Such methods might also be used to collect physiological signatures, including temperature, 

pulse, and blood pressure.235

Beyond imaging technologies, the next major gains 

in facial recognition are likely to come from a combination of increased processing power and improvements in the use of artificial intelligence (AI). AI refers 

generally to the subfield of computer science dealing 

with algorithms focused on tasks traditionally requiring human analysis such as visual perception, speech 

recognition, and abstract problem solving. AI is often 
90

associated with the concept of “big data” due to the 

fact that these large datasets generally must be analyzed through computational methods. AI algorithms 

are generally very useful for analyzing and interpreting this data to reveal patterns, trends, and embedded 

associations.236 Within the field of AI, a distinct area 

called machine learning involves the use of specialized 

algorithms able to “learn” from the data itself and offer predictive models by analyzing patterns. One class 

of machine learning known as “deep learning” uses 

mathematical formulas to replicate “neural networks” 

by simulating the analytical processes of the human 

brain. These methods have demonstrated significant 

potential for improving the accuracy and power of existing biometric modalities, particularly in the areas of 

facial, speaker, and voice recognition.

The concept for neural networks has existed for 

several decades; however, practical applications were 

limited by computing power and the lack of sufficiently large datasets to optimize “training.” This training 

occurs by process repetition that “teaches” the computer how to recognize increasingly more complex 

features contained within the data, such as from collections of images or sound files. For example, one of 

the more famous technical challenges in the field of 

image recognition involves the relatively simple human task of teaching a computer to identify images of 

cats among a collection of unrelated photos. Recently 

Google researchers demonstrated a significant improvement at this task, using deep learning methods 

to sort through and identify target images from some 

10 million examples contained in YouTube videos.237

Similar methods have been used to increase the speed 

and accuracy of commercial image recognition applications such as those used by Facebook, Google, 

Microsoft, and Twitter.238
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The application of deep learning methods and 

neural networks are likely to offer significant performance improvements for image and video analytics, 

potentially enabling rapid searches and matching 

from enormous repositories of media files. This task 

represents one of the major technical hurdles in the 

era of “big data.” For example, the FBI’s biometric 

database is expected to contain some 52 million facial 

images by 2015, while the Department of State already 

has an even larger holding of photos from visa applicants.239 Sorting through these repositories presents 

a relatively well-defined task of matching within a 

closed system containing uniform data and strict controls on image quality. However, a much greater technical challenge is involved in exploiting information 

from unstructured databases such as sifting through 

massive amounts of social media content in search of 

meaningful correlations. This is especially true for collections containing ambiguous, unformatted content 

or files with associated metadata. Other challenges involve matching heterogeneous data such as the task of 

comparing images obtained from visible light versus 

near-infrared systems.

Several recent prototypes have demonstrated how 

neural networks might help to overcome some of these 

limitations such as the ability to identify facial images 

captured at an angle or when partially occluded. Researchers have applied these methods to improve automated detection algorithms for video files, moving 

the technology a step closer to having the capability of 

real-time analysis of digitized video or closed-circuit 

television (CCTV) footage.240 The future gold standard 

for facial recognition will be the capability for noncontact collection, at a reasonable range, among multiple 

individuals, with near real-time identification. Some 
92

currently available technologies hint at the direction 

this capability may move. For example, one new commercial product is able to capture facial images from 

live video feeds, then instantly compile any identityrelated content from social media sites and other open 

sources to create a real-time subject profile.

There have also been recent improvements in 

existing technologies for enhancing the speed and 

flexibility of biometric enrollment, such as collecting 

iris images in just a few seconds and the capability 

for noncontact fingerprint capture. High-resolution 

cameras are now producing recognition-quality images from a distance of several meters under certain 

conditions, with some prototypes even extending this 

effect range out to 100-200 meters.241 One potential 

use of this capability was recently demonstrated by a 

group claiming to have obtained a useable fingerprint 

biometric from the German Defense Minister derived 

only from a digital photograph, taken at a distance, 

during a public speaking engagement. The image reportedly was obtained using a commercially available 

digital camera and a common fingerprint scanning algorithm.242 Various methods are also in development 

for noncontact fingerprint recognition on mobile and 

other digital devices. Similar progress is being made 

in capturing useable iris data under bright sunlight 

conditions—a major limiting factor of this modality.243

Another company has demonstrated an iris scanning 

prototype able to collect usable images at more than 

30 yards against a stationary subject; however, such 

technologies remain years away from operational 

use.244 Other efforts are exploring variations on the basic modalities such as using the blood vessel patterns 

in the eye which, unlike iris and retinal scans, can be 

captured with standard digital cameras rather than 

infrared light emitters.
93

Among the standard modalities, another important goal involves improved methods for multi-modal 

data integration, specifically bringing together iris, fingerprint, and facial imaging into a composite “pattern 

of life” profile of individual subjects.245 Beyond these 

technologies, a number of nontradition biometric modalities are gradually moving toward operational use, 

including vascular pattern, ear structure, hand geometry, gait, odor, palm print, and electrocardiograph. 

Another nontraditional modality now being used on 

a wider scale is the identification of scars, marks, and 

tattoos. The FBI’s next generation identification database recently integrated this capability, offering both 

image matching and queries based on descriptive key 

words.246 While not uniquely identifying in isolation, 

such tools offer another means for multi-modal verification that potentially improves accuracy when linked 

with other attributes such as DNA profiles, biographic 

data, behavioral patterns, and digital and media signatures.

Another modality with recent gains in performance has been the field speaker recognition. As a 

biometric, automated speaker recognition systems can 

“extract, characterize, and recognize the information 

in the speech signal conveying speaker identity.”247

Generally this can be done with just a brief voice sample providing enough data to measure an individual’s 

unique vocal traits and produce a biometric quality 

“voiceprint.” Many current efforts in the field have 

been focused on identity authentication and fraud detection for use in banking services, call center operations, and for digital device interface. This has led to 

a rapid expansion in the number of individuals using 

this technology with one industry research study estimating that, by 2019, some five billion people will 
94

have created a personal voiceprint.248 Experts speculate that some 65 million unique voiceprints may already be contained in U.S. law enforcement and intelligence community databases—a number certain to 

expand exponentially in coming years.249

As a biometric, voice offers several distinct advantages over other modalities. It has been called an 

“invisible” biometric since it does not require direct 

contact for collection as voiceprints can be obtained 

from a range of sensors (microphone, telephones, 

computers, etc.). However, there are several limitations of current technology, including difficulty in 

cross-channel comparisons between landline, cellular, 

or microphone recordings, as well as issues dealing 

with ambient noise, language variations, or unusual 

phonation such as whispering. Another major technical challenge entails dealing with overlapping speech 

from multiple speakers on a single audio sample.250

Once voice data is collected, the traditional approaches of speaker recognition generally apply statistical models to represent variations in sound forms, 

or phonemes, unique to an individual’s voice.251 Hidden Markov Models (HMM) have been the dominant 

statistical technique for text-dependent speaker recognition (when there is prior knowledge of the text 

to be spoken). A related method, known as Gaussian 

Mixture Models, is often used for text-independent, or 

extemporaneous speech, not requiring cooperation by 

the speaker. In both cases, digitized voice patterns are 

compared to stored voiceprints in order to produce a 

recognition decision. Similar statistical models are applied to other pattern recognition tasks such as natural 

language processing, handwriting, and gesture analysis. The HMM method is behind the Army’s current 

VIBES prototype used for identifying and tracking 
95

individuals and analyzing tactical communications 

networks.252

As with facial recognition technology, recent advances in machine learning and increased processing 

power have led to significant performance improvements. Neural networks are now being used on large 

databases of sound files for identification of progressively more complex combinations of phonemes. This 

enables the programs to “learn” over time, without 

human intervention and better deal with ambiguity 

in syntax as well as variations in usage and dialect. 

Deep learning methods and neural networks may also 

provide approaches for enhancing low-quality signal data by filtering out unwanted sounds, removing 

noisiness, and even disambiguating multiple voices on 

a single channel.253 This could also overcome some of 

the technical difficulties encountered with audio type 

mismatch. The Defense Advanced Research Projects 

Agency (DARPA) is exploring solutions to some of 

these issues through its Robust Automatic Transcription of Speech program aimed at separating speech 

from background noise as well as related tasks such as 

determining the languages being spoken from an audio file and isolating key words within that sample.254

Researchers are also experimenting with novel 

applications derived from voiceprint data such as determining an individual’s emotional state. One such 

example involves biomedical screening of neurological disorders using a combination of markers derived 

from motor control functions linked to speech and 

facial expression. A team from the Massachusetts Institute of Technology’s (MIT) Lincoln Laboratory recently demonstrated an application using facial and 

vocal biomarkers as a predictive tool for analyzing 

an individual’s cognitive state.255 The Department of 
96

Homeland Security is exploring related capabilities 

for use in checkpoint screening, for example, using a 

combination of facial movements, voice samples, and 

physiological sensors for detecting respiratory, cardio, 

thermal, and iris reaction as a means of identifying 

hostile intent or suspicious behavior among crowds.256

Some researchers speculate that these methods 

may soon enable computers to surpass humans in the 

ability to perceive basic emotions. DARPA has demonstrated the utility of automated tools for identifying psychological distress, depression, and anxiety.257

There have also been successful examples of applying 

facial recognition software to code emotional expressions based on distinct movements of facial muscles, 

eyes, cheeks, lips, and other features. A recent commercial prototype achieved an accuracy rate on the order of 97 percent in the identification of six basic emotions, and 77 percent for some compound emotions.258

Beyond the standard modalities, the growing field 

of behavioral biometrics potentially offers a range 

of new methods for establishing identity by indirect 

means. In general terms, behavioral biometrics refers 

to characteristics that are learned or acquired over 

time rather than those based primarily on biology. For 

instance, these include skills, style, preference, knowledge, and motor skills that people use to accomplish 

everyday tasks.259 Some common examples in current 

use involve handwriting, keystroke, mouse dynamics, 

or walking gait analysis. Other examples include the 

identification of distinguishing behavioral patterns 

derived from activities such as email routines, device 

interactions, and credit card usage.

Behavioral biometrics has a number of advantages 

over traditional biometrics, most notably the potential 

for remote or noncompliant collection. Although be-
97

havioral biometrics are often less accurate than biologic measures, they can be used in conjunction with 

other modalities or applied indirectly for identifying 

characteristics of individuals within a larger population. For instance, this might include identifying a 

group of individuals with a distinguishing attribute 

such as word knowledge, mathematical aptitude, or 

skill at a specific task such as speaking a foreign language or flying an airplane. While not uniquely identifying in isolation, such contextual information could 

be useful in analyzing networks by categorizing the 

functions of individual members or network nodes.

Other activity patterns such as email usage or 

web surfing offer the possibility of deriving unique 

user identification with the advantage of nonobtrusive collection. Multiple studies have demonstrated 

how unique behavioral profiles can be derived from 

the peculiarities of email usage, including message 

stylization, temporal activity, structure, and other 

variables.260 This has obvious applications for resolving ambiguous identities derived from user accounts 

shared by multiple individuals or accessing public 

computers. Similar applications have been developed 

to spot aberrant behavior on social media platforms 

and detecting fake Twitter and Facebook accounts. 

Analysis of behavioral patterns can help confirm the 

authenticity of anonymous actors on social media 

or detect pattern similarities between multiple users 

across platforms. Behavioral biometrics can also be applied to help identify online deception campaigns by 

analyzing linguistic cues, usage patterns, social connections, and physical locations to classify the identities behind the posts. For example, similar methods 

were recently used to analyze postings from Russian 

military personnel suspected of mobilizing for operations in Ukraine.261
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Another example of a common behavioral biometric is the use of credit card patterns for fraud protection by the banking industry. This technique applies 

statistical methods to identify aberrant behaviors such 

as unusual transactions, new geographical locations, 

or simultaneous card use at multiple locations. One 

vendor in the field has refined a method for analyzing 

more than 400 bio-behavioral, cognitive, and physiological traits to create highly individualized user 

profiles.262 These techniques have become commonplace in bank security and other applications where 

anonymous metadata can be used to analyze an individual’s unique behavioral attributes. A team from 

MIT’s Media Lab demonstrated this capability when 

they identified some 90 percent of individuals from a 

sample group based only on the dates and locations of 

a handful of credit card transactions combined with 

social media metadata. This was accomplished without prior knowledge of names, addresses, or other information relating to the cardholder.263

Behavioral biometrics are also modernizing approaches to traditional modalities such as writing 

analysis, a technique used for many years as an identifying tool in document forensics. However, in the 

digital age, there are fewer written documents available for analysis; therefore, methods have evolved for 

analysis of “digital handwriting” or dynamic signatures based on the unique way a user types, holds, 

and manipulates a digital device. These techniques 

are now being applied for identity authentication 

with mobile devices using cognitive-biometric attributes based on factors such as handedness, hand 

tremor, eye-hand coordination, keystroke analysis, 

and other identifiable patterns embedded within 

human-machine interactions.264 Research has found 
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these behavioral patterns to be “complex, nuanced, 

and instinctive,” therefore a highly accurate method 

of identifying individuals.265 Some recent commercial 

examples of advanced dynamic signature authentication can track unique identity attributes across four 

dimensions, including pressure, swiftness, and shape 

of finger strokes against a touch screen, as well as the 

speed and acceleration of gestures.266

Specialists in the field have also applied deep 

learning techniques for analysis of gesture and motion 

control to enable unique activity recognition. Some 

research suggests that these motion patterns are just 

as identifying as fingerprints. For example, one recent 

experiment used unique “egocentric video biometrics” derived from raw video footage taken from head 

and body-mounted cameras. In this case, the unique 

biometric markers were obtained from as little as four 

seconds of footage by tracking “optical flow” through 

each frame of the video.267 One potential application 

would be an ability to locate all videos shot by a single 

user from within a large database of digital files, even 

without descriptive metadata. Another recent demonstration applied deep learning methods to improve the 

performance of built-in sensors on mobile devices (accelerometers, cameras, and microphones) in order to 

classify whether a user was performing certain kinds 

of activities.268 Similar techniques have been developed 

for biometric authentication from computer mouse 

manipulation and fitness tracking devices. Most of 

these behavioral biometric applications are now being used for improving device security; however, they 

potentially provide a means of remote identification. 

This could be invaluable information when combined 

with precise geolocation from a mobile device or correlation with other social media activity. As humans 
100

increasingly maintain near-continual interaction with 

their digital devices, the field of behavioral biometrics 

will offer techniques well-suited for deriving identity 

information from these activities.

Another field of emerging biometric modalities is 

the area of “soft biometrics.” These are characteristics 

with identifying qualities but lacking distinctiveness 

and permanence to differentiate positively between 

any two individuals.269 Examples include traits such 

as gender, hair color, height, weight, body proportions, eye color, or ethnicity. Another category of soft 

biometrics includes activity-based characteristics such 

as uniquely acquired skills or specialized knowledge. 

Although less identifying than standard modalities, 

soft biometrics can offer some advantages over biologic and behavioral attributes as a tool for screening 

and analysis. First, much of the data can be obtained 

nonobtrusively or derived from low-quality video 

footage, thus making it useful for high-throughput 

surveillance applications. Second, unlike technical signatures, soft biometrics can be expressed more easily 

in natural language, thus making for easier categorization based on verbal descriptions of physical characteristics.270 This issue relates to the so-called “semantic 

gap” or the difference between how humans verbally 

express distinct physical attributes versus how they 

are represented as biometric signatures.

The central problem of the “semantic gap” is that 

physical descriptions from eyewitness reports are not 

easily translatable into machine language for computer assisted search and analysis. Some new approaches 

in this area involve improved translation of semantic 

descriptions into usable categorizations for an automated search of images and surveillance video.271 This 

kind of semantic labeling potentially provides a pow-
101

erful enhancement for computer-assisted screening. 

This may lead to improved algorithms for matching 

eyewitness sketches with digital face images. Other 

researchers have demonstrated how generic attribute 

descriptions such as clothing types, hair color, and 

gender can be used as criteria for an automated search 

through large volumes of surveillance video data in 

order to find persons matching a particular subject 

profile.272 While these methods are currently limited 

by factors such as video quality and crowd density, 

with improvement they may offer another useful tool 

in combination with other biometric modalities and 

contextual information. See the following scenario.

Based on the INTERPOL alert, the suspect was held 

for additional questioning. After refusing to discuss 

his identity or explain the discrepancies from the 

passport photo, the Directorate General of the Turkish 

National Police sent his basic biometric data (iris, fingerprint, and facial scan) through the local INTERPOL 

liaison office for analysis. Within the hour, the INTERPOL HQ notified the Turkish authorities that the passport had been reported stolen some 6 months prior. 

INTERPOL also reported that the suspect had been 

declared a person of interest by the U.S. Government 

based on a match from the FBI’s biometric repository. 

Based on this information, the U.S. legal attaché at the 

American Embassy in Ankara requested that Turkish 

authorities keep the man in custody pending further 

investigation. At the request of the U.S. ambassador, 

a regionally based Identity Resolution team was deployed to Istanbul to assist Turkish authorities in the 

ongoing investigation.
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OPERATIONAL ATTRIBUTION

The next section discusses emerging technologies 

that could be applied to the task of operational attribution, or the linking of an identity to specific locations, incidents, and activities. These include a variety 

of biometric, forensic, and analytical tools that could 

be leveraged to assist in developing a subject’s operational profile. See the following scenario.

Twenty-four hours later, the Identity Resolution team 

arrived in Istanbul and began work in collaboration 

with local authorities. During the preceding day, a 

check of U.S. Government databases resulted in a 

confirmed match from the DoD’s Biometric Enabled 

Watchlist. The information revealed that the suspect 

had been held at the U.S. detention facility at Camp 

Bucca, Iraq, in 2008 following a Special Operations 

raid against a suspected IED production facility. The 

man was released to Iraqi authorities some 17 months 

later. The report further identified the man as a British 

national of Jordanian descent. Additional information 

provided by Dutch authorities through INTERPOL 

indicated that the man had resided temporarily in 

Amsterdam a year earlier; however, he departed that 

country and was suspected of participating as a foreign fighter in Syria. Based on this initial information, 

the Turkish authorities provided the Identity Resolution team with access to the man’s cell phone and 

personal items. During a subject interview the team 

collected a voiceprint for analysis and received permission to take soil samples and trace materials from 

his clothing, as well as a buccal swab for DNA analysis. Turkish authorities also provided CCTV video of 

the man’s arrival and movements through the airport.

Over the last decade, there have been major advances in the use of DNA analysis for military and 
103

security functions. As a tool for identity verification it 

offers the advantages of being unique and unalterable. 

Like fingerprints, DNA is a latent identifier, meaning 

it can be obtained forensically without direct physical 

contact with a subject. It also offers a higher matching confidence than any other biometric and forensic 

method. For example, there is approximately an 86 

percent chance of correctly matching a latent fingerprint against a record in the FBI’s biometric database. 

Conversely, DNA forensics provides near perfect 

statistical assurance of matching when samples are 

properly collected and processed.273

Currently, the most common technique used for 

forensic DNA analysis is the short tandem repeat 

(STR) process that evaluates specific STR regions 

found in nuclear DNA.274 The STR profile provides the 

individual’s unique genotype based on regions of the 

chromosome, called loci, having a high degree of variability between individuals. STR analysis is typically 

used for body fluids, skin cells, bones, and hairs, and 

offers an extremely accurate method for identifying a 

particular individual as the source of an evidence sample.275 STR analysis has advantages over other methods in terms of sensitivity, shorter processing time, 

and a higher level of statistical discrimination. Once 

a sample is processed, this data can be translated into 

a CODIS-compatible message format then uploaded 

into a DNA database.276 Y-STR analysis is a similar 

method used exclusively on the male Y-chromosome. 

This technique is often applied for genealogical and 

paternity testing, sexual assaults, missing persons, 

and some intelligence applications; however, due to 

the fact that multiple relatives share the same Y-DNA, 

it is not yet possible to derive unique identifications 

from this analysis. The expeditionary forensic labs de-
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ployed in Iraq and Afghanistan generally used a combination of these methods of nuclear DNA analysis.

Traditionally, the sequencing process has been 

the major bottleneck of DNA forensics; however, process improvements, miniaturization, and automation 

have greatly reduced many of these hurdles. Recent 

advances in mircofluidics, the handling of very small 

volumes of fluids, has revolutionized DNA analysis 

and enabled high-throughput sequencing. These new 

“rapid DNA” methods generally describe a fully automated (hands-free) process for developing CODIS 

core STR profiles from reference samples. Various 

commercial providers offer fully integrated systems 

capable of performing STR analysis in less than 2 

hours. Rapid DNA instruments are not yet approved 

for domestic crime scene forensics; however, the FBI 

Laboratory and federal agencies, including the U.S. 

Army Crime Laboratory, are conducting test and evaluation for future accreditation and operational use.277

Another limitation of current technologies is the 

need for trained technicians working in controlled 

laboratory environments; however, recent prototypes 

are overcoming many of these challenges. One recent 

example is the accelerated nuclear DNA equipment 

(ANDE) system, a device about the size of a desktop printer and able to process as many as five DNA 

samples in 90 minutes. Importantly, this system can 

be operated by nontechnical personnel, outside of a 

laboratory setting, and reliably produce forensic-quality STR profiles that are fully compatible with CODIS 

message format.278 U.S. Special Operations forces have 

already used similar devices in forward locations and, 

within the next 5 years, hope to field-test a miniaturized, battery-powered version of DNA readers, allowing forces to collect DNA in a tactical environment 

and instantly compare results to a remote database.279
105

In the next major advancement, next generation 

sequencing (NextGen DNA sequencing) methods will 

soon offer powerful new capabilities using singlenucleotide polymorphisms (SNP) analysis based on 

variations at a single site in the DNA. These sites are 

scattered throughout the human genome and play 

a large role in determining an individual’s susceptibility to disease and response to vaccines and other 

environmental agents. NextGen DNA sequencing 

methods use exome sequencing (a subset of all DNA 

across all chromosomes) to analyze thousands more 

SNPs at a time and produce a higher level of discrimination than current STR analysis, even from degraded 

samples.280 These techniques can help to overcome 

one of the significant limitations of current STR analysis, specifically the challenge of dealing with DNA 

mixtures involving genetic materials from multiple 

individuals. This is a significant concern in scenarios 

such as IED factories or crime scenes where more than 

one person has handled physical evidence. NextGen 

DNA sequencing will offer more robust capabilities 

for characterization of these identities as well as decreasing the cost and speed of analysis.281

The SNP profiles derived from NextGen DNA sequencing methods have also demonstrated significant 

potential for analyzing variations between individual 

genomes, opening a door to novel applications beyond basic identification matching. These may include 

deriving bio-geographic ancestry, extended kinship 

mapping, and surname prediction from genetic samples.282 Such methods could be particularly useful in 

geographic regions where there are dense familial and 

tribal relationships that are closely related to targeted 

networks. These methods have already been used for 

deriving familial relationships from unknown DNA 

samples as part of criminal investigations.283
106

Even with the increased analytical power of current methods, SNP genotyping still uses less than 0.1 

percent of human genetic material. However, advances in high-throughput sequencing will soon offer the 

potential for whole genome analysis, leading to improved prediction of visible physical characteristics, 

also known as forensic DNA phenotyping.284 Full genome analysis has already demonstrated reasonable 

accuracy in predicting features such as eye and hair 

color. Some experts suggest it may soon predict skin 

color, freckling, baldness, hair curliness, tooth shape, 

and even age. One research team recently demonstrated a three-dimensional modeling program able 

to depict a “genetic face” based on sex and ancestry 

mix determined from the DNA. The image can then 

be refined based on 24 genetic variants from 20 genes 

known to be involved in facial variation.285 While intriguing, such applications are still new, controversial, 

and not yet permissible in most legal proceedings.

The complexity and expense of whole genome 

sequencing has dropped dramatically in recent 

years, from millions of dollars a decade ago to a few 

thousand at present. In addition to a wide range of 

medical applications, this capability could also offer 

improved techniques for epigenetic analysis, or interpretation of gene expressions caused by specific environmental exposure to chemicals, radiation, or other 

agents. This could lead to more detailed subject trait 

profiles based on nothing more than residual DNA. 

Another potentially useful application emerging from 

whole genome sequencing will be powerful capabilities for metagenomic analysis of materials obtained 

from environmental samples. This could offer unique 

characterization of microbial ecologies, for instance, 

analyzing the recent movements of a subject based 
107

on evidence from trace soil, microbial organisms, or 

pollen samples. This might also be used to detect recent exposure to chemicals or precursor materials of 

interest.286 See the following scenario. 

As the investigation continued, the Identity Resolution team obtained initial results from the DNA analysis. The suspect’s profile matched an unidentified 

sample catalogued in late-2009 from forensic evidence 

found at an IED factory in Iraq. Several days later, 

the team received results from geo-forensic analysis 

of trace samples obtained from the suspect’s clothing. This determined that the dried soil on the man’s 

shoes and pollen in his clothing were consistent with 

soil texture and vegetation types common in northern 

Syria, suggesting recent travel from that region. This 

suspicion was corroborated by exploitation of the cell 

phone’s metadata that provided time and locations of 

the suspect’s most recent travel. The cell phone also 

contained personal identifying information for a number of associates, including names, addresses, telephone numbers, e-mail addresses, chat user names, 

and recent text message exchanges with individuals 

suspected of having links to a known foreign fighter 

facilitation network. The browser cache also contained 

a record of several recently accessed social media sites. 

A number of short videos were extracted from the 

phone’s on-board memory to be analyzed and compared against other media samples posted to online 

forums frequented by the foreign fighter community. 

Additionally, the phone’s internal sensor data was 

used to create a biometric profile from stored gesture 

patterns and keystroke history. Based on the developing body of evidence, the U.S. legal attaché requested 

that Turkish authorities continue to hold the suspect 

as the investigation continued.
108

MAPPING THE NETWORK

Once operational history is established, the next 

step involves linking identity to other activities, locations, and a wider network of relevant actors. One 

of the major analytical challenges in this task is uncovering the implicit network connections contained 

within enormous amounts of unstructured data. As 

Director of National Intelligence James Clapper recently explained, in the era of big data, the challenge 

is finding the needles without having to deal with the 

haystacks.287 The next section explores some recent 

advances in areas such as video analytics, natural 

language processing, and data management, specifically exploring how these technologies are improving 

methods for sorting, correlating, and analyzing information from a variety of sources. See the following 

scenario.

As the investigation continued, DNA kinship analysis 

compared with the CODIS database suggested a possible familial link between the suspect detained in Turkey and another man recently arrested during an FBI 

raid against a suspected terror cell in Atlanta, Georgia. 

The cell was accused of conducting pre-operational 

planning for a possible attack against a U.S. Federal 

building. Materials seized during the raid included 

a laptop with pdf. files containing handwritten notes 

and bomb making instructions. Handwriting and content analysis from the document files suggested a likely match to several documents found in the files of the 

suspect in Turkey. The laptop seized during the Atlanta raid also contained several video clips taken at an 

unidentified terrorist training camp. Gesture control 

analysis from the clips matched those of the video segments obtained from the suspect’s cell phone, suggesting that a single individual using that phone camera 
109

had recorded all of the training camp videos. Content 

analysis of the video, including activities, landscape, 

and individuals, also matched similar videos posted 

on several social media sites frequented by extremist 

groups. From this data and other supporting information, the location of the camp was narrowed to a few 

likely sites. These materials also provided evidence 

of a direct operational linkage between the suspect in 

Turkish custody and the planning cell in Atlanta.

The last few years have seen significant advances 

in video analytics, particularly the use of artificial intelligence for improved content analysis. This field of 

“computer vision” focuses on these automated methods for processing, interpreting, and analyzing image 

content. Most conventional methods for sorting and 

correlating bulk video data still rely on metadata information such as titles and text comments associated 

with the media rather than the actual content. This 

presents a major challenge for conducting large-scale 

multimedia data retrieval, queries, and analysis based 

only on metadata keywords. Another approach has 

been to use model-based semantic analysis for identifying content relating to a predefined set of activities. 

As with other tasks involving complex probabilistic 

modeling, HMM and related methods has been one 

approach for pattern recognition of video content.288

Artificial intelligence and deep learning methods 

have potential for improving content-based analysis 

and aiding discovery of contextual relationships between video data, other media, and information. One 

recent demonstration involved training a computer to 

distinguish between a group of men playing Frisbee 

and a herd of elephants walking in the grass.289 While 

seemingly a basic task for humans, this represented a 

major technical achievement for computers. The evo-
110

lution of such tools will be critical for rapidly cataloging and analyzing the millions of digital images and 

video files generated and uploaded onto the Internet 

each day—an amount that already greatly exceeds 

what can be done by human analysis. One example 

highlighting the great potential in this area was recently demonstrated in a commercial application using deep learning software to analyze videos rapidly 

and recognize some 10,000 different objects and types 

of scenes within the collection of clips.290 The software 

was even able to derive and identify abstract descriptive concepts such as “fun.”

A related analytical challenge concerns training 

computers to interpret and extract highly nuanced 

content features such as defined social roles. For example, one recent prototype demonstrated the ability to 

identify the most important individual among a group 

of people appearing together in a photo.291 This sort of 

task depends on deriving complex semantic meaning 

from subtle visual cues, then translating these visual 

indicators into useful text descriptions that can be understood by humans or used in computational analysis. Researchers at Google recently demonstrated such 

a method of translating complicated images into short 

descriptive sentences. The approach involved training neural networks on two separate but related tasks: 

first, processing images into mathematical representations based on content, then, second, translating 

that information into a human-readable text. In this 

case, the neural network was “trained” from tens of 

thousands of images that were already tagged with 

descriptions written by humans.292

There is related work in this field on improved 

methods for document authentication and determining image provenance, a particularly useful tool, giv-
111

en recent examples of image manipulation being used 

as part of information campaigns. For example, during the 2014 offensive by the Islamic State into Iraq, 

militants posted pictures of captured helicopters and 

tanks allegedly taken from Iraqi security forces; however, analysis later revealed that many of the images 

posted on social media sites turned out to be older file 

photos. Iran, Russia, and others have also used manipulated images for the purpose of disinformation and 

deception. One effort focused on overcoming such 

challenges is the Intelligence Advanced Research Projects Activity “Finder” program designed to help analysts locate non-geotagged imagery from collections 

of photographs or video clips. This could leverage online crowd-sourcing tools or use image matching techniques based on large reference databases of overhead 

and ground-based images, including features such as 

elevation data, surface geology, ground vegetation, 

local geography, and embedded cultural information. 

Prototype testing has demonstrated some initial success using data-rich video and imagery files containing relatively recognizable features. However, even in 

the case of remote locations with less embedded data, 

the tools were still able to significantly narrow down 

the search region in some cases.293

As with image and video data, the exponential 

growth of text-based document and media files presents similar technical challenges for analysts. This is 

the general focus of Natural Language Processing 

(NLP) technologies, a field of computer science dealing with the task of enabling computers to derive 

meaning from human language inputs. NLP touches 

on many of the functional areas already discussed, 

including tasks such as speech recognition, machine 

translation, and automated document classification. 
112

As with image analysis, the ability to derive contextual meaning from large amounts of unformatted data 

and bulk media files is critical for entity discovery and 

mapping network relationships. 

Traditional NLP approaches have applied machine 

learning approaches and statistical models to make 

probabilistic determinations based on comparisons 

to a corpus of sample texts available in digital databases and online collections. This is essentially how 

Google’s voice and translation applications work, by 

making comparisons to previously translated documents found around the web then determining most 

likely matches.294 Once natural language inputs are 

converted into homogenous data sets, this enables 

easier discovery of identity attributes and relationship 

extraction.

As in other areas, advances in artificial intelligence 

show great promise for improving the accuracy of 

NLP methods, particularly in the areas of information 

retrieval and data correlation. Importantly, many of 

these techniques go far beyond basic machine translation and optical character recognition. These methods 

may soon enable computers to derive deeper semantic 

meaning and higher-level functions such as sentiment 

analysis of social media and advanced data visualization based on embedded linkages between people, 

places, and activities contained within large amounts 

of unstructured data. 

Some current applications are already appearing in 

the commercial sector, for instance, using NLP methods for interpreting social media inputs and logically 

recommending appropriate hashtags and improved 

methods for understanding a user’s implicit intentions behind a series of words entered into a search 

engine.295 One artificial intelligence company has 
113

applied NLP technologies to enable real-time analysis of multiple communications channels, including 

news sites, blogs, online forums, and social media to 

help identify emerging trends and unique behavioral 

patterns.296 Importantly, these systems are moving 

toward an ability to derive contextual meaning without requiring a human analyst in the loop. Both the 

Defense and Intelligence Advanced Research Projects 

Agencies have multiple programs in this area and are 

attempting to apply these technologies for defense 

and intelligence use. See the following scenario.

Over the next several weeks, the investigation continued with an analysis of the suspect’s digital presence 

through social media activity, browsing history, and 

contacts obtained from the phone’s data cache. This 

analysis revealed several useful linkages between various on-line profiles, tweets, geotagged posts, images, 

and video content linked to a blog. The correlation of 

these digital signatures provided a basic pattern of life 

based on recent activities, locations, interactions, and 

communications through various social media channels. As the team reconstructed the suspect’s digital 

identity map, the investigators were able to infer latent 

social networks based on correlation with other users 

sharing similar patterns, habits, locations, and semantic content. This evidence suggested links to several 

key nodes of a foreign fighter facilitation network and 

the location of a previously unknown processing facility for newly arriving recruits. Further analysis of propaganda videos posted to several social media sites 

produced additional high-probability matches with 

the suspect’s facial image, gait, and voiceprint. Separately, the results of a full genome DNA analysis suggested a possibility of the suspect’s recent exposure to 

precursor chemicals commonly used as accelerants for 

bomb making.
114

The final section examines how analytical tools 

for social media and other “big data” sources may offer new capabilities for linking identity signatures to 

larger network structures, possibly even enabling improved methods for predictive analysis. Social media 

analysis offers another means of identity discovery 

and attribute linage across wider networks. Many approaches in this area are based on the study of graph 

analytics, an interdisciplinary field concerned with 

information extraction, data management, and visualization.297 These tasks involve the use of statistical 

methods for detecting implicit network structures, 

actor attributes, and embedded relationships. 

Some of the complicating factors in this analysis include dealing with the inherent subjectivity of textual 

data, ambiguous identities, and uncertainty in classifying the relationships between these entities.298 Many 

of these cutting edge tools for social media analysis 

are found in the commercial sector and are currently 

being used for purposes such as marketing and political forecasting. However, many of these tools clearly 

have dual-use applications for national security, disaster relief, and law enforcement functions. For example, applications able to geolocate Tweeter feeds 

and Facebook posts have been used to analyze behavioral patterns and identity attributes from anonymous 

social media actors. 

Others researchers in the field of social media 

analysis have been able to construct generic identity 

profiles based on implicit attribute information embedded within the content and structure of network 

communications such as linguistic patterns, social 

connections, and expressed preferences. For example, 

researchers have demonstrated that identity attributes 

such as age, gender, occupation, education, and even 
115

personality traits, can be accurately predicted from 

the analysis of web browsing activity. Other studies 

have shown how social media patterns can accurately 

reveal characteristics such as sexual orientation, ethnicity, and religious and political views.299 While not 

explicitly identifying, such data points could provide 

a strong basis for basic identity profiling and screening.300 Related work has exploited social media content 

to generate relatively reliable personality recognition 

predictions for traits such as extraversion, neuroticism, 

agreeableness, conscientiousness, and openness.301

Much of this work is based on new insights about 

how the structure of an individual’s social network 

creates a “digital footprint” that, in some instances, 

can predict latent personality traits and behavioral 

propensities such as job success, drug use, infidelity, 

and general level of emotional happiness.302 Researchers have also applied statistical-relational analysis for 

improved attribute prediction and classification based 

on how specific variables are transmitted between 

individuals based on their social and organizational 

ties.303 For instance, this kind of analysis has been used 

for identifying behavioral propensities of individual 

actors within larger social communities and used for 

tasks such as risk-based predictive screening. For example, one UK government screening program for 

terrorism prevention has developed a model of behavioral indicators as part of its vulnerability assessment framework, applying 22 variables associated 

with factors such as engagement, intent, and capability. Together these indicators can suggest an individual’s vulnerability for being drawn toward violent 

extremism.304

Much of the information required for such analysis 

may be gleaned from publically available social media 
116

data and other open sources. Recently, a team from 

the King’s College International Centre for the Study 

of Radicalization and Political Violence (ICSR) used 

social media information and other open data to build 

profiles of some 700 Western foreign fighters who had 

traveled to Syria as combatants, compiling as many as 

72 data points for each individual. This dataset provided useful insights into identities and operational 

profiles of individuals joining the movement, as well 

as significant details about the methods and pathways of their recruitment.305 Of particular note, this 

evidence established how the foreign fighter cohorts 

evolved from clearly identifiable social clusters and 

groups of friends typically drawn from the same geographic areas.306

This example highlights one of the classic applications of social network theory, specifically the dynamics of social influence. Recent work in the field has 

demonstrated how these techniques can even identify 

leadership actors and functional roles among members of a network based only on general patterns of 

activity, communications, and individual attributes.307

Related areas of research have also demonstrated how 

social network analysis can be used for deception detection, authorship attribution, sentiment analysis, 

and opinion mining. This is similar to the idea behind 

“crowd-sensing” or using aggregate data derived from 

mobile devices to model general behavioral patterns 

as well as predicting more specific variables of interest.308 These methods may also offer new approaches 

for dynamic social network analysis, a subfield that 

examines changes in the behavior of social networks 

over time, an important tool for detecting anomalous 

behavior within networks and among the individual 

actors operating within them.309
117

The ongoing conflict in Syria has provided a rich 

dataset for experimentation. Since the start of hostilities foreign fighters have generated millions of social 

media posts and enormous amounts of digital content. 

By some accounts, the Islamic State is linked to some 

90,000 message posts each day, involving as many as 

30,000 active followers around the globe.310 One team 

of researchers recently used this data to develop a detailed network representation of the intercommunal 

dynamics involved in the conflict. This analysis revealed a surprising level of internal complexity among 

the factions, including implicit divisions among the 

protagonists that were not obvious from anecdotal reporting. Importantly, this analysis identified distinct 

cohorts of social media followers observing the conflict from outside Syria and provided insights into the 

dynamics of the external media campaign.311

Nearly all of these analytical techniques are based 

on the use of “big data” for identity discovery and attribute correlation. This common, and often misused, 

term refers not only to the size of the data sets involved but also to the computational tools and statistical methods used to derive meaning from the data. 

Dealing with the variety and volume of information 

contained in these massive datasets has created a situation that is “overwhelming and incomprehensible to 

humans” and requires improved methodologies for 

turning this data into coherent information.312 Some 

of these techniques have already been described at 

length; however, a few examples illustrate how these 

large datasets might be used in the future for improving identity discovery, operational attribution, and 

network mapping. 

One recent example of a “big data” tool is the DARPA-sponsored Memex search engine. This application 
118

builds infographics based on relationships between 

web pages, specifically those residing in so-called 

“dark web” networks like Tor where IP addresses are 

obscured.313 Unlike standard search engines designed 

to sift through text and images, Memex uses embedded information such as geo-coordinates encoded 

into photos, handwritten numbers within images, 

and even background scenes from photos that can 

be compared independently from the other objects in 

the picture. From this information Memex produces a 

networked schematic based on the implicit relationships embedded within the nonindexed data, including things like phone numbers, street addresses, and 

individual names.

Another example of a big data-type project is 

the Global Database of Events, Language and Tone 

(GDELT), a worldwide, multidecade, geo-referenced, 

daily event dataset. The GDELT monitors print, broadcast, and news media sources in over 100 languages, 

across every country in the world, using machine 

translation technology to access non-English sources. 

The aim of the project is to catalog human, societalscale behaviors down to a micro-level. For example, 

it can use dozens of data fields to capture all available 

details about a particular event such as a small terrorist attack, the individual actors involved, and their 

roles.314 The Institute for the Study of Violent Groups 

has developed another example with a database containing detailed information on some 223,000 incidents 

of violent extremism and transnational crime involving over 43,000 unique individuals with connections 

to over 3,000 violent groups. This dataset is based on 

traditional media, social media, video content, police 

reports, and court documents, among other sources. 

The database structure contains as many as 1,500 vari-
119

ables to help categorize individual events then visualize the linkages within the data.315

Some far reaching “big data” applications eventually may offer improved methods for predictive analysis going beyond the targeting of individuals already 

identified as threats. For example, they may be able to 

provide a means to screen preemptively specific actors 

or highlight environmental conditions that are more 

likely to produce radicalization among certain individuals. For instance, the Global Terrorism Database 

project at the University of Maryland contains detailed 

records of some 125,000 terrorist incidents dating back 

to the 1970s, using 40 to 120 variables for each incident record.316 From within this repository, researchers have developed a more detailed dataset of some 

1,500 people radicalized toward violent and nonviolent extremism in the United States since World War 

II, including biographic information, criminal records, 

social networks, and personal histories. This data has 

enabled researchers to identify some important commonalities among the group including the importance 

of social networks and presence of key facilitators, but 

also variables such as age, marital status, and proxy 

measures for social assimilation. 

While critics charge that the promise of big data 

has been perpetually oversold, it likely offers one of 

the few viable alternatives for refining risk profiling 

models and behavioral indicators linked to identitybased screening, as well as potentially supporting 

strategies for preemptive interventions before social 

alienation moves individual actors toward radicalization and violence. See the following scenario.

As the investigation continued, the analysis of the 

suspect’s social media network provided dozens of 
120

investigative leads in the United States and several 

European countries. Linkages confirmed the suspect’s 

involvement with other actors known to be part of a 

foreign fighter facilitation network responsible for recruiting and moving new fighters to an initial entry 

point in Syria for indoctrination and training. These 

leads prompted several preemptive interventions focused on individuals already in the recruitment pipeline. Exploitation of additional photos and content derived from social media posts provided evidence that 

narrowed down the possible location of the Syrian 

training site and identified several other individuals 

operating at the location. Based on the subject’s operational profile and significant evidence of personal 

connections to the foreign fighter network, the UK 

government filed criminal charges against the individual and initiated a formal extradition request with 

Turkish authorities.

ASSESSING IWAR 1.0

The preceding scenario and discussion surveyed 

just a small portion of the emerging technology landscape and considered how these tools may be used 

for the waging of iWar in the future. This exploration 

was speculative rather than predictive, acknowledging the uncertain course of technology innovation and 

the highly dynamic nature of the threat environment. 

These factors highlight the enormous challenges facing 

military planners and policymakers as they attempt to 

align defense strategy with research and development 

goals focused on the most likely, and pressing, future 

risks to U.S. national security.

In this sense, the story of iWar may be read as 

a cautionary tale concerning the hazards of misplaced prediction of future threats and the technologies needed to defeat them. Much of what has been 
121

described in this monograph evolved not as a matter 

of premeditated design, but rather from a decade of 

ad hoc adaptations in response to exigencies created 

by an unexpected adversary. This situation required 

that the U.S. national security apparatus quickly reorient on a new kind of threat, then develop supporting 

doctrines, technologies, and methods needed to meet 

these challenges.

The ability of the military, intelligence community, and domestic security entities to recognize what 

was needed and eventually put these plans into action is a testimony to the ingenuity and dedication of 

these organizations and individuals. However, it also 

highlights the enormous cost and risk of making the 

wrong decision. As the U.S. Army’s operating concept 

points out: 

thinking clearly about future armed conflict requires 

consideration of threats, enemies and adversaries, 

anticipated missions, emerging technologies, opportunities to use existing capabilities in new ways, and 

historical observations and lessons learned.317

Even under the best of circumstances, this represents 

a formidable task.

In assessing the larger significance of iWar, a few 

definitions should be clarified. The term “warfare” 

describes the way in which nations make war, specifically the tools, techniques, and methods of applying 

coercive power against an adversary on the battlefield. 

Extending from this, the “character of war” describes 

this phenomenon of warfare contextualized within a 

larger milieu of cultural, technological, social, political, and bureaucratic influences. The rise of iWar reflects a significant change on both levels of analysis.
122

As a new model of warfare, iWar formed a distinct 

set of tools, techniques, and methods that evolved 

over a relatively short period of time from theory 

into practice in direct response to specific operational 

challenges on the battlefield. These challenges primarily centered on the task of fighting an adversary 

organized as networks and the operational need to 

identify, screen, and target the individual combatants 

within these networks. In this sense, iWar represents 

something truly unique about the changing character 

of modern war, specifically the phenomena of individual, named combatants becoming legitimate objects of 

state warfare and the focus of operational targeting.

The larger question remains as to whether this 

operational paradigm represents a fundamental and 

enduring shift in how America organizes, equips, and 

wages such conflicts in the future. Alternately, this 

episode may simply represent a transient diversion 

from the national security establishment’s traditional 

focus on conventional, maneuver-style warfare waged 

against state-based adversaries. The answer to this 

question implies a larger issue of whether or not the 

United States will return to a model of warfare where, 

once again, combatants are anonymous agents on the 

battlefield, targeted on the basis of status rather than 

identity. At this point, the answer to this question is 

not entirely obvious.

FUTURE IWAR AS POLICY AND STRATEGY: 

CAUTIONS AND CONSIDERATIONS

In the weeks following the attacks of 9/11, the 

United States embarked upon a conflict for which it 

was largely ill-prepared. The military had spent the 

decades of the Cold War developing doctrines and 
123

technologies geared for the prospect of major conventional force engagement against state-based adversaries. The ambiguity of the security environment during 

the 1990s did little to alter the powerful force of inertia 

or divert the national security establishment from this 

relatively narrow strategic focus.

However, the rise of disruptive, nonstate actors 

such as al-Qaeda and the experience of two protracted 

counterinsurgency campaigns brought about a fundamental shift in how national security decisionmakers 

viewed the principal threats to U.S. interests. The response to these new threats involved major structural, 

technical, and functional transformations undertaken 

in the midst of conflict. As a result, the United States 

ultimately developed a ruthlessly effective operational-level capability for identifying and targeting small 

cells and individual combatants across the battlefield. 

It was equally successful in applying these techniques 

toward focused counterterrorism targeting across a 

wider expanse of ungoverned spaces and locations 

where local security forces were either unable or unwilling to engage these threats.

On the domestic front, the United States leveraged 

similar technologies and information management 

tools in support of identity-based screening strategies that have kept borders, transportation networks, 

and American citizens remarkably safe since 9/11.

However, at the same time, these remarkable tactical 

and technical achievements have not fully delivered 

desired political end-states at the strategic level. This 

is particularly true with regard to stabilizing volatile 

regions and stemming the perpetual regeneration of 

individual threats emerging from such places as Afghanistan, Iraq, Yemen, Somalia, Syria, and Libya.
124

These setbacks arguably have altered American 

strategic calculus, possibly for generations. As the 

President’s most recent National Security Strategy 

emphasizes, America has shifted away from a strategy based on fighting costly, large-scale ground wars 

in lieu of: 

a more sustainable approach that prioritizes targeted 

counterterrorism operations, collective action with 

responsible partners, and increased efforts to prevent 

the growth of violent extremism and radicalization 

that drives increased threats.

Yet, even with these words of restraint, the President 

emphasized that: 

outside of areas of active hostilities, we endeavor to 

detain, interrogate, and prosecute terrorists through 

law enforcement. However, when there is a continuing, imminent threat, and when capture or other actions to disrupt the threat are not feasible, we will not 

hesitate to take decisive action.318

In word and deed the current administration has 

suggested that many of the foundational pillars of the 

iWar paradigm will continue to play a part of U.S. 

strategic approaches. However, this also suggests a 

national security strategy that will primarily focus 

on risk mitigation rather than military victory. This 

may be a strategy that does not offer any clear political end-state other than the immediate tactical goal of 

identifying and neutralizing the most pressing threats 

to key U.S. interests and citizens. If that is the case, 

then this will be a conflict waged primarily with information rather than conventional weapons, emphasizing technical tools and cognitive skills more than 
125

firepower and maneuver. This mode of warfare will 

continue to strain the traditional bureaucratic boundaries and functional separations between law enforcement, military actions, and intelligence activities. As 

one commentator recently observed, the nature of 

these modern security threats “makes it virtually impossible to draw neat lines between war and peace, 

foreign and domestic, emergency and normality.”319

As this mode of warfare becomes normalized, it is also 

likely to challenge accepted privacy norms, perhaps 

with significant implications for the larger issue of 

identity and how it relates to national security.

In concluding this monograph, a few cautions and 

recommendations are offered. First, it must be kept 

in mind that iWar evolved primarily as a strategy of 

tactics. It represented methods and tools designed to 

treat symptoms but not the disease. The doctrinal and 

technical innovations of iWar dealt with a very specific operational challenge of identifying, screening and 

targeting network-based adversaries and individual 

combatants. However, it has very little to offer in terms 

of dealing with the underlying causes of instability 

and political violence. For this reason, the methods of 

iWar must be applied with circumspection and in a 

manner that does not conflate targeting with strategy. 

General McMaster has cautioned against this “raiding mentality” and the fallacy that strategic victories 

can be achieved simply by identifying critical network 

nodes, then eliminating them with surgical precision. 

A related concern is that, as the United States demonstrates increasing prowess in these methods, it will 

produce overconfidence in technologies of iWar. This 

caution stems from a very consistent American predilection for techno-scientific solutions to every national 

security problem, even when these repeatedly fail to 
126

fulfill their initial promise. A decade of refining these 

methods has clearly demonstrated an important place 

for technologies that “remove a violent extremist’s 

greatest defense—anonymity;” however, the utility of 

these tools must not be oversold.320

Regardless of future strategic choices, the innovations resulting from a decade of waging iWar have 

produced extraordinary capabilities at the operational 

and tactical levels. These skills, tools, and methods 

must continue to mature even as persistent operational demands subside. This includes the recent doctrinal, technical, organizational, and policy innovations 

that have been central to waging this mode of warfare. By all indications, various forms of hybrid war 

and irregular threats will persist into the near future. 

This will inevitably involve U.S. military activities in 

ungoverned spaces, with weak identity regimes and 

adversaries determined to use anonymity for operational advantage. Therefore, the need for identity 

verification on the battlefield and on borders will not 

diminish. 

In terms of doctrine innovation, there is a great 

risk of stagnation, from the Army in particular, as 

military forces gravitate back toward traditional focus 

on conventional force engagement against state-based 

adversaries. iWar, as waged over the last decade, will 

remain an inherently land force-centric mode of warfare. It cannot be fought from the air and will remain 

dependent upon ground forces for useful intelligence 

gathering and performing effective targeting. For this 

reason, the emerging doctrines of identity intelligence 

and identity operations must continue to mature and 

be integrated into a flexible concept of full spectrum 

military operations.
127

Among the major technical challenges will be detecting and exploiting a range of nonstandard signatures and data sources (cyber, open source, social media, biometrics, and forensics) and integrating these 

with conventional collection streams for improved 

situational awareness. This will require continued improvements in areas such as standoff biometric collection and expeditionary forensics, as well as advances 

in data integration and information sharing between 

the defense, intelligence, and homeland security communities. This will demand new methods for data 

processing and analytical tools designed for dealing 

with large amounts of unstructured information—all 

representing enormous technical challenges that cannot wait for the next crisis.

In terms of organizational change, it is time to 

acknowledge that current and future threats will 

continue to erode the distinctions between external 

defense and internal security. An individual of interest encountered in a combat zone may also have relevance to a customs agent at John F. Kennedy Airport, 

New York, a police officer conducting a routine stop 

in Tucson, Arizona, or a counterterrorism analyst at 

the CIA. Bureaucratic interests, technical barriers, and 

over-classification must not inhibit robust information 

sharing among these entities. This realization should 

inform a more deliberate process to reconfigure the national security apparatus with seamless integration as 

the basic organizing principle. However, this discussion must also include a re-evaluation of the legal and 

policy frameworks to ensure appropriate protections 

of information, as well as necessary checks on power 

in order to address civil liberty and privacy concerns.

At the outset, this analysis noted how the iWar 

paradigm has challenged fundamental aspects of the 
128

Westphalia construct, including many of the legal 

foundations that have defined the conduct of state 

warfare in the modern age. Over the last decade, the 

United States has demonstrated a remarkable tactical 

proficiency at waging this kind of warfare, particularly the identification, screening, and targeting of individual combatants around the globe. However, the 

continuing debates over disposition of detainees held 

in Guantanamo, Cuba, acrimony over the scope and 

application of the AUMF, and lingering concerns over 

nonbattlefield lethal targeting all suggest that many of 

the issues regarding the conduct of iWar remain unresolved, if not controversial.

If the United States continues to operate under 

the premise that threats posed by individual combatants now represent a significant national security 

concern and, therefore, are a legitimate object of state 

warfare, then there must be a corresponding legal 

and ethical framework guiding how this kind of war 

will be waged. In order to be effective, this construct 

must have equivalent scope and authority as existing 

structures that have defined the terms and limits of 

conventional conflict for decades. For America, this is 

a particularly salient need. As the world’s remaining 

superpower, its security must rest upon unquestioned 

legitimacy in the use of military force no matter the 

choice of strategy, tools, or methods.

ENDNOTES

1. Sun Tzu, The Art of War, Thomas Cleary, trans., Boston, 

MA: Shambhala Pocket Classics, 1991.

2. Micah Zenko, “The U.S. Just Launched Its 500th Drone 

Strike,” Defense One, November, 21, 2014, available from www.

defenseone.com/threats/2014/11/us-just-launched-its-500th-drone-
129

strike/99722/?oref=defenseone_today_nl. Also, the New American 

Foundation, Long War Journal, and Bureau of Investigative Journalism all monitor U.S. drone strikes taking place outside the “active combat zones” of Iraq, Afghanistan, and Libya. The 500 total strikes in Pakistan, Yemen, and Somalia represent an average 

among the range of estimates from these organizations as of November 2014. This number includes a total of seven U.S. citizens 

killed by drone strikes during the Obama administration.

3. Jo Becker and Scott Shane, “Secret ‘Kill List’ Proves a Test of 

Obama’s Principles and Will,” The New York Times, May 29, 2012.

4. Linda Robinson, Paul D. Miller, John Gordon IV, Jeffrey 

Decker, Michael Schwille, and Raphael S. Cohen, Improving Strategic Competence: Lessons from 13 Years of War, Santa Monica, CA: 

RAND, 2014, p. 26.

5. John Arquilla and David Ronfeldt, Networks and Netwars: 

The Future of Terror, Crime, and Militancy, Santa Monica, CA: 

RAND, 2001.

6. Thomas L. Friedman, The Lexus and the Olive Tree, New 

York: Anchor Books, 2000.

7. Arquilla and Ronfeldt.

8. Defense Science Board Task Force on Defense Intelligence, 

Counterinsurgency: Intelligence, Surveillance, and Reconnaissance Operations, Washington, DC: Office of the Under Secretary of Defense 

for Acquisition, Technology, and Logistics, February 2011, p. 52.

9. Martin van Creveld, The Rise and Decline of the State, New 

York: Cambridge University Press, 1999, pp. 162-163.

10. Jean-Jacques Rousseau, “The Social Contract,” Victor 

Gourevitch, ed., The Social Contract and Other Writings, New York: 

Cambridge University Press, 1997, p. 51.

11. Gabriella Blum, “The Individualization of War: From War 

to Policing in the Regulation of Armed Conflicts,” Austin Sarat, 

Lawrence Douglas, and Martha Merrill Umphrey, eds., Law and 

War: An Introduction, Redwood City, CA: Stanford University 

Press, 2014, p. 52.
130

12. For elaboration on this concept, see Gabriella Blum, “The 

Dispensable Lives of Soldiers,” Journal of Legal Analysis, Vol. 2, 

No. 1, Spring 2010, pp. 115-147.

13. 10 U.S.C. § 948a: US Code, Section 948A defines “unprivileged enemy belligerent” as an individual (other than a privileged 

belligerent) who (A) has engaged in hostilities against the United 

States or its coalition partners; (B) has purposefully and materially supported hostilities against the United States or its coalition 

partners; or (C) was a part of al Qaeda at the time of the alleged 

offense under this chapter.

14. Samuel Issacharoff and Richard Pildes, “Targeted Warfare: Individuating Enemy Responsibility,” New York University 

Law Review, Vol. 88, No. 5, November 2013, p. 1,521.

15. In addition to dozens of journal articles on this topic, 

see “Forensics and Warrant Based Targeting,” March 2010, Ft. 

Leavenworth, KS: Center for Army Lessons Learned (CALL), 

for a detailed discussion of how this concept has been applied 

operationally.

16. van Creveld, p. 163.

17. There are certainly exceptions to this rule in American 

history, notably targeting against such figures as Poncho Villa, 

Hitler, Yamamoto, Gadhafi, Noriega, Mohammed Aideed, and 

Osama Bin Laden. However, as a practical matter prior to 9/11, 

leadership targeting was generally applied as a discrete military 

objective that was adjunct to the larger political purpose of the 

conflict. As such, it has not been a central feature of warfighting strategies or formalized as part of doctrinal approaches until 

recently.

18. Benjamin Rhodes, Deputy National Security Advisor, 

from an interview with NPR. Quote reprinted in Bill Chappell, 

“U.S. Won’t Rule Out Attack In Syria To Hit Islamic State,” 

August 21, 2014, available from www.npr.org/blogs/thetwoway/2014/08/21/342165273/u-s-won-t-rule-out-attack-in-syria-to-hitislamic-state.
131

19. Rosa Brooks, “There’s No Such Thing as Peacetime,” 

Foreign Policy, March 13, 2015, available from www.foreignpolicy.

com/2015/03/13/theres-no-such-thing-as-peacetime-forever-war-terrorcivil-liberties/.

20. John Abizaid and Rosa Brooks, Recommendations and Report of the Task Force on US Drone Policy, Washington, DC: Stimson 

Center, June 2014, p. 12.

21. Among others, see recent discussions on this topic by 

Issacharoff and Pildes, and Gabriella Blum.

22. U.S. Joint Chief of Staff, Joint and Coalition Operational 

Analysis Division (J7), Decade of War Volume 1: Enduring Lessons 

from the Past Decade of Operations, Washington, DC: U.S. Joint 

Chiefs of Staff, June 15, 2012, p. 2.

23. U.S. Department of the Army, Field Manual (FM) 3-60, The 

Targeting Process, Washington, DC: Headquarters, Department of 

the Army, November 26, 2010, p. B-1.

24. Office of the Under Secretary of Defense for Acquisition, 

Technology, and Logistics, Report of the Defense Science Board Task 

Force on Defense Biometrics, Washington, DC: Defense Science 

Board, March 2007.

25. General David Petraeus, Commander, U.S. Central 

Command, Multi-National Force-Iraq, “Counterinsurgency 

Guidance,” June 21, 2008.

26. Targeting doctrine defines “high-value individuals,” or 

HVIs, as “persons of interest (friendly, adversary, or enemy) who 

must be identified, surveilled, tracked, and influenced through 

the use of information or fires.” See FM 3-60, p. B-1.

27. Christopher J. Lamb and Evan Munsing, Secret Weapon: 

High-Value Target Teams as an Organizational Innovation, Washington, DC: National Defense University Press, March 2011, p. 33.

28. Stanley McChrystal, “It Takes a Network: The New Front 

Line of Modern Warfare,” Foreign Policy, February 21, 2011, available from www.foreignpolicy.com/2011/02/21/it-takes-a-network/.
132

29. Carlotta Gall, “Night Raids Curbing Taliban, but Afghans 

Cite Civilian Toll,” The New York Times, July 8, 2011. Also see Tom 

Peter, “Afghanistan: NATO’s Night Raids Cause More Harm 

than Good, Report Says,” The Christian Science Monitor, September 

19, 2011.

30. Charles Faint and Michael Harris, “F3EAD: Ops/Intel 

Fusion Feeds The SOF Targeting Process,” Small Wars Journal, 

January 31, 2012. 

31. U.S. Joint Forces Command, Commander’s Handbook for 

Attack the Network, Suffolk, VA: Joint Warfighting Center, Joint 

Doctrine Support Division, May 20, 2011.

32. Steve Ressler, “Social Network Analysis as an Approach 

to Combat Terrorism: Past, Present and Future Research,” Homeland Security Affairs, Vol. 2, No. 2, July 2006.

33. See Duncan Watts, Six Degrees: The Science of a Connected 

Age, New York: W. W. Norton, 2003; and Small Worlds: The Dynamics of Networks between Order and Randomness, Princeton, NJ: 

Princeton University Press, 1999.

34. Ajay Mehra, Daniel Brass, Giuseppe Labianca, and Stephen Borgatti, “Network Analysis in the Social Sciences,” Science, 

Vol. 323, No. 5916, February 13, 2009, pp. 892-895.

35. FM 3-24, Counterinsurgency, Washington, DC: Headquarters, Department of the Army, December 15, 2006, Appendix B.

36. John A. Nagl, “The Evolution and Importance of Army/

Marine Corps Field Manual 3-24, Counterinsurgency,” Foreword 

to the U.S. Army/Marine Corps Counterinsurgency Field Manual 

by the United States Army and United States Marine Corps, Chicago, IL: University of Chicago Press, 2007, available from www.

press.uchicago.edu/Misc/Chicago/841519foreword.html.

37. For useful analysis on the integration of SNA into the Army’s counterinsurgency doctrine, see David Knoke, “It Takes a 

Network: The Rise and Fall of Social Network Analysis in U.S. 

Army Counterinsurgency Doctrine,” and John A. Nagl, “Constructing the Legacy of Field Manual 3-24,” Joint Forces Quarterly, 

Vol. 58, pp. 118-120.
133

38. FM 3-24, Appendix B.

39. FM 3-60, p. B-1.

40. Bob Woodward, “Why Did Violence Plummet? It Wasn’t 

Just the Surge,” The Washington Post, September 8, 2008, available 

from www.washingtonpost.com/wp-dyn/content/article/2008/09/07/ 

AR2008090701847.html.

41. Joint Publication (JP) 3-24, Counterinsurgency, Washington, 

DC: U.S. Joint Chiefs of Staff, November 2013, p. XVI.

42. Joint Center for Operational Analysis, Operation IRAQI 

FREEDOM, January 2007 to December 2008 The Comprehensive 

Approach: An Iraq Case Study, Norfolk, VA: U.S. Joint Forces 

Command, February 2010, p. 14.

43. See Army Electronic Publication (ATP) 2-33.4 Intelligence 

Analysis, August 2014, JP 2.01-3 Joint Intelligence Preparation of the 

Operational Environment, June 2009, and FM 3-60.

44. Some critics remain skeptical with regard as to how fully 

the military has embraced these ideas and whether they still influence thinking about future warfighting concepts, particularly as 

many leaders advocate a return to conventional warfighting approaches and doctrines. See Knoke for this contrasting viewpoint.

45. See United States Marine Corps (USMC) IdOps Strategy 2020 and U.S. Department of the Navy, Marine Corps Order 

5530.17, Marine Corps Identity Operations (IdOps), November 

13, 2012.

46. Ibid.

47. Anthony Smith and Mark Schaefer, “More Than Just Biometrics: Why Marine Corps Identity Operations are Critical to 

MAGTF Mission Success,” Marine Corps Gazette, Vol. 98, No. 5, 

May 2014.

48. Identity intelligence (I2) appeared for the first time as part 

of recognized doctrine in October 2013 in the updated version of 

JP 2.0, Joint Intelligence, then more recently in the latest version of 
134

JP 3-05, Special Operations, July 2014, where it described I2 as “the 

collection, analysis, exploitation, and management of identity 

attributes and associated technologies and processes.”

49. JP 3-26, Counterterrorism, Washington, DC: U.S. Joint 

Chiefs of Staff, October 24, 2014, p. V-5.

50. Robert O. Work and Shawn Brimley, 20YY: Preparing 

for War in the Robotic Age, Washington, DC: Center for a New 

American Security, 2014, p. 17.

51. Antoine Bousquet, “Cyberneticizing the American War 

Machine: Science and Computers in the Cold War,” Cold War 

History, Vol. 8, No. 1, 2008.

52. Emily Mushen and Jonathan Schroden, Are we Winning? 

A brief History of Military Operations Assessment, Washington, DC: 

CNA Center for Stability and Development, 2014.

53. Charles Shrader, History of Operations Research in the U.S. 

Army, Volume III, 1973-1995, Washington, DC: Office of the Deputy Under Secretary of the Army for Operations Research, 2009.

54. William Westmoreland, “Address to the Association of 

the U.S. Army,” October 14, 1969, cited in Antoine Bousquet, Cybernetic Warfare: Computers and the Cold War, Meeting of International Studies Association, San Diego, CA, March 22, 2006.

55. Robert Tomes, “The Cold War Offset Strategy: Assault 

Breaker and the Beginning of the RSTA Revolution,” War on the 

Rocks Blog, November 20, 2014, available from www.warontherocks.

com/2014/11/the-cold-war-offset-strategy-assault-breaker-and-thebeginning-of-the-rsta-revolution/.

56. Mark Mazzetti, The Way of the Knife, New York: Penguin 

Books, 2014, pp. 94-101. Also see Andrew Callam, “Drone Wars: 

Armed Unmanned Aerial Vehicles,” International Affairs Review, 

Vol. XVIII, No. 3, Winter 2010.

57. Jeremiah Gertler, U.S. Unmanned Aerial Systems, Washington, DC: Congressional Research Service, January 3, 2012.
135

58. Amitai Etzioni, “The Great Drone Debate,” Military 

Review, Vol. 93, No. 2, March-April 2013, p. 2.

59. Andrew Callam, “Drone Wars: Armed Unmanned 

Aerial Vehicles,” International Affairs Review, Vol. XVIII, No. 3, 

Winter 2010.

60. Abizaid and Brooks, p. 11. Unclassified reporting suggests 

a significant increase in the use of such strikes during the Obama 

administration with 33 strikes in 2008 and 54 in 2009, killing at 

least 20 militant leaders. See Simon Frankel Pratt, “Crossing off 

Names: The Logic of Military Assassination,” Small Wars and 

Insurgencies, Vol. 26, No. 1, pp. 3-24.

61. Pratt. 

62. Bill Roggio, “U.S. Military Confirms it Killed Islamic State, 

Shabaab Leaders in Airstrikes,” Long War Journal Blog, February 

11, 2015, available from www.longwarjournal.org/archives/2015/02/

us_military_confirms.php#ixzz3RWChIqrJ.

63. Micah Zenko, Reforming U.S. Drone Strike Policies, Washington, DC: Council on Foreign Relations, January 2013, p. 8.

64. U.S. Department of Defense, Defense Science Board Task 

Force on COIN and ISR Operations, Washington, DC: Office of 

the Undersecretary of Defense for Acquisition, Technology, and 

Logistics, February 2011, p. 65.

65. Office of the Under Secretary of Defense for Acquisition, 

Technology, and Logistics, “Report of the Defense Science Board 

Task Force on Defense Biometrics,” Washington, DC: Defense 

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66. Anthony Iasso, “A Critical Time for Biometrics and Identity Intelligence,” Military Intelligence Professional Bulletin, JulySeptember 2013, pp. 39-40.

67. Timothy S. McWilliams and Nicholas J. Schlosser, “U.S. 

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136

The New York Times, July 13, 2011, available from www.nytimes.

com/2011/07/14/world/asia/14identity.html?_r=0.

68. Jody Kieffer and Kevin Trissell, “DOD Biometrics: Lifting 

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69. Myra Gray, “Defending the U.S. with Biometrics,” Infosecurity, Vol. 6, No. 6, September/October 2009, pp. 24–25.

70. Spencer Ackerman, “U.S. Holds on to Biometric Database 

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71. Additional Training for Leaders and More Timely Transmission 

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72. David Pendall and Cal Sieg, “Biometric-Enabled Intelligence in Regional Command–East,” Joint Forces Quarterly, Vol. 72, 

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73. U.S. Army, Commander’s Guide to Biometrics in Afghanistan: 

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for Leaders and More Timely Transmission of Data Could Enhance the 

Use of Biometrics in Afghanistan, p. 8.

74. “Biometrics in Afghanistan: The Eyes Have It,” The Economist, July 7, 2012, available from www.economist.com/node/21558263.

75. For a useful overview discussion of the issues and summary of various recidivism rate estimates, see Jennifer Elsea and 

Michael John Garcia, “Wartime Detention Provisions in Recent 

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76. Martin Chulov, “ISIS: The Inside Story,” The Guardian, December 11, 2014, available from www.theguardian.com/world/2014/

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137

77. David Axe, “CSI Somalia: Interpol Targets Pirates,” Wired 

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78. Antonia Greene, “Including Biometrics in Deployment 

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79. Deputy Secretary of Defense, Authority to Collect, Store, and 

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80. VIBES, Leidos Product Publication, available from www.

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81. Erik Bowman, “Game Changers in DoD Biometrics,” 

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82. U.S. Marine Corps, Marine Corps Forensic Enterprise Strategy, April 20, 2010, p. 3, available from www.mccdc.marines.mil/Portals/172/Docs/SWCIWID/SWAAB/IW%20Reader/MCFES_Final%20

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83. Additional Planning and Oversight Needed to Establish an 

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84. Oliver Herion, “Expeditionary Forensic Support to Joint 

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85. Douglas Shontz, DNA as Part of Identity Management for the 

Department of Defense, Santa Monica, CA: RAND, 2010, p. 7.
138

86. Michael Johnston, “Expeditionary Forensics: The Warrior’s Science Revealing the Hidden Enemy,” Military Police, 

Spring 2009, pp. 5-7.

87. “U.S. Used Lab in Afghanistan to Confirm bin Laden’s 

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88. Patrick Tucker, “Special Operators are Using Rapid DNA 

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89. Marine Corps Forensic Enterprise Strategy, p. 5.

90. Additional Planning and Oversight Needed to Establish an Enduring Expeditionary Forensic Capability, p. 1.

91. Joop Voetelink, “EvBO: Evidence-Based Operations, How 

to Remove the Bad Guys from the Battlefield,” Journal of International Law of Peace and Armed Conflict, Vol. 4, 2013, p. 195.

92. Thomas B. Smith and Marc Tranchemontagne, “Understanding the Enemy: The Enduring Value of Technical and Forensic Exploitation,” Joint Forces Quarterly, Vol. 75, No. 4, October 

2014, p. 124.

93. Anthony Iasso, “A Critical Time for Biometrics and Identity Intelligence,” Military Intelligence Professional Bulletin, JulySeptember 2013, pp. 39-40.

94. United States Government Accountability Office, “Defense Forensics: Additional Planning and Oversight Needed to 

Establish an Enduring Expeditionary Forensic Capability,” June 

2013, available from www.gao.gov/assets/660/655546.pdf.

95. Sandra I. Erwin, “As Defense, Intelligence Agencies 

Drown in Data, Technology Comes to the Rescue,” Nation Defense 

Magazine, November 2014.

96. Stephen Mayhew, “U.S. Defense Department Biometrics 

Database Upgraded,” DoD Press Release, as reported in Biometrics 
139

Update, December 16, 2014, available from www.biometricupdate.

com/201412/u-s-defense-department-biometrics-database-upgraded 

and www.dote.osd.mil/pub/reports/FY2013/pdf/army/2013dodabis.pdf.

97. Patrick Tucker, “Jihadi John and the Future of the Biometrics Terror Hunt,” Defense One, February 27, 2015, available from 

www.defenseone.com/technology/2015/02/jihadi-john-and-futurebiometrics-terror-hunt/106263/?oref=defenseone_today_nl.

98. Siobhan Gorman, “How Team of Geeks Cracked Spy 

Trade,” The Wall Street Journal, September 4, 2009, available from 

www.wsj.com/articles/SB125200842406984303.

99. Andy Greenberg and Ryan Mac, “How A ‘Deviant’ Philosopher Built Palantir, A CIA-Funded Data-Mining Juggernaut,” 

Forbes, August 14, 2013, available from www.forbes.com/sites/

andygreenberg/2013/08/14/agent-of-intelligence-how-a-deviantphilosopher-built-palantir-a-cia-funded-data-mining-juggernaut/.

100. George I. Seffers, “Army’s Talking Technology,” Signal Magazine, September 2011, available from www.afcea.org/

content/?q=armys-talking-technology.

101. Erik Bowman, “Game Changers in DoD Biometrics,” and 

“Biometric Data Storage and Biometric Intelligence,” presentations at the 2013 Biometrics Big Data Symposium For Defense, 

Intelligence, Homeland Security and Law Enforcement, June 18, 

2013, Washington, DC, p. 11, available from www.semanticommunity.info/@api/deki/files/27723/Bowman-TTC_Biometric_BigData_

Conf_fl13.pdf.

102. For a useful recent overview, see Lisa Seghetti, “Visa 

Waiver Program,” William L. Painter, ed., Selected Issues in 

Homeland Security Policy for the 114th Congress, Washington, DC: 

Congressional Research Service, May 2015, pp. 38-41.

103. Alison Siskin, Visa Waiver Program, Washington, DC: 

Congressional Research Service, February 2014, pp. 5-8.

104. “FBI Announces Biometrics Suite’s Full Operational Capability,” FBI Press Release, September 23, 2014, available from 

www.fbi.gov/news/stories/2014/september/fbi-announces-biometrics-
140

suites-full-operational-capability/fbi-announces-biometrics-suites-fulloperational-capability.

105. Aliya Sternstein, “Homeland Security To Roll Out Biometrics Along the Border This Summer,” Defense One, January 

28, 2015, available from www.defenseone.com/technology/2015/01/

homeland-security-roll-out-biometrics-along-bordersummer/103968/?o

ref=defenseone_today_nl. Also see “OBIM Wants Info on Biometric 

Matching Systems,” PlanetBiometrics, April 28, 2015, available from 

www.planetbiometrics.com/article-details/i/2981/desc/obim-wants

-info-on-biometric-matching-systems/, accessed April 29, 2015. See 

more at www.planetbiometrics.com/article-details/i/2981/desc/obimwants-info-on-biometric-matching-systems/#sthash.qFSOtHkQ.dpuf.

106. Amanda Vicinanzo, “Vulnerabilities in Coast Guard’s 

Biometric System May Impede Identification of Suspected Terrorists,” Homeland Security Today, March 14, 2015, available from 

www.hstoday.us/briefings/daily-news-analysis/single-article/vulnerabilities-in-coast-guards-biometric-system-may-impede-identification-ofsuspected-terrorists/bc0e386816bcc3e25b64ebcbd7ee3309.html.

107. Stephen Mayhew, “Obama’s New National Security 

Strategy Makes a Push for Biometrics Data Sharing,” Biometric 

Update, February 10, 2015, available from www.biometricupdate.

com/201502/obamas-new-national-security-strategy-makes-a-push-forbiometrics-data-sharing.

108. This information includes, but is not limited to, biometric data: finger scans and digital facial photographs; encounter 

data: place and date of visa issuance; and biographic data: name, 

date of birth, gender, physical details, and visa issuance or visa 

refusal data.

109. The Consular Consolidated Database (CCD). For a complete discussion, see Ruth Ellen Wasem, Immigration: Visa Security 

Policies, Washington, DC: Congressional Research Service, June 

2014, p. 6.

110. Wasem.

111. Kathleen Kiernan, ‘‘Counterintelligence and Law Enforcement,’’ Jennifer Sims and Burton Gerber, eds., Vaults, Mirrors 
141

& Masks: Rediscovering U.S. Counterintelligence, Washington, DC: 

Georgetown University Press, 2009, p. 159.

112. John Wagner, Written testimony of CBP Office of Field 

Operations Acting Deputy Assistant Commissioner John Wagner 

for a House Committee on Oversight and Government Reform, 

Subcommittee on National Security hearing titled “Border Security Oversight, Part III: Border Crossing Cards and B1/B2 Visas,” 

November 14, 2013, available from www.dhs.gov/news/2013/11/14/

written-testimony-cbp-house-oversight-and-government-reform-subcommittee-national.

113. Currently, citizens of 38 countries may travel to the United States without a visa as part of the Visa Waiver Program based 

on the 2007 Homeland Security Presidential Directive 6 legislation. 

For a useful discussion, see a recent post on the Lawfare blog by 

Nathan Sales, “Is the Visa Waiver Program a Threat to our National Security?” February 2, 2015, available from www.lawfareblog.com/2015/02/is-the-visa-waiver-program-a-threat-to-our-nationalsecurity/.

114. Martin Rudner, “Intelligence-Led Air Transport Security: 

Pre-Screening for Watch-Lists, No-Fly Lists to Forestall Terrorist 

Threats,” International Journal of Intelligence and Counterintelligence, 

Vol. 28, No. 1, 2015, p. 48.

115. National Counterterrorism Center, Terrorist Identities 

Datamart Environment (TIDE) Factsheet, August 2014, available 

from www.nctc.gov/docs/tidefactsheet_aug12014.pdf.

116. Adam Goldman, “More than 1 Million People are Listed in U.S. Terrorism Database,” The Washington Post, August 

5, 2014, available from www.washingtonpost.com/world/nationalsecurity/more-than-1-million-people-are-listed-in-us-terrorism-database/2014/08/05/a66de30c-1ccc-11e4-ab7b-696c295ddfd1_story.html.

117. Ibid.

118. Ken Kroupa, “An Inside Look at DoD’s DNA,” presentation delivered at the 2010 Biometrics Consortium, September 

21-23, 2010, Tampa, FL, available from www.biometrics.org/bc2010/

presentations/RapidDNA/kroupa-An-Inside-Look-at-DoD-s-DNA.pdf.
142

119. “Report of the Defense Science Board Task Force on 

Defense Biometrics,” p. 31.

120. FBI CODIS: National DNA Index (NDIS), FBI Homepage, available from www.fbi.gov/about-us/lab/biometric-analysis/

codis/ndis-statistics.

121. Gefrides and Welch.

122. Patrick Tucker, “Special Operators are Using Rapid 

DNA Readers,” Defense One, May 21, 2015, available from www.

defenseone.com/technology/2015/05/special-operators-are-using-rapiddna-readers/113383/?oref=defenseone_today_nl.

123. Thomas B. Smith and Marc Tranchemontagne, “Understanding the Enemy: The Enduring Value of Technical and 

Forensic Exploitation,” Joint Forces Quarterly, Vol. 75, Fall 2014, 

pp. 122-128.

124. “Next Generation Identification, FBI Announces Biometrics Suite’s Full Operational Capability,” FBI Press Release, 

September 23, 2014, available from www.fbi.gov/news/stories/2014/

september/fbi-announces-biometrics-suites-full-operational-capability/

fbi-announces-biometrics-suites-full-operational-capability.

125. Robert Schroeder, “A Strategic Evolution: A Path to Border Security,” Holding the Line in the 21st Century, Washington, 

DC: U.S. Customs and Border Protection, p. 38.

126. Justin Lee, “Customs’ Facial Recognition Technology 

Trial Raises Privacy Concerns,” Biometric Update, May 29, 2015, 

available from www.biometricupdate.com/201505/customs-facial-recognition-technology-trial-raises-privacy-concerns.

127. Authorization for the Use of Military Force (AUMF), Joint 

Resolution 23, 107th Cong., 1st sess., September 14, 2001. Also see 

Public Law § 2(a), 115 Stat, p. 224.

128. John O. Brennan, “The Efficacy and Ethics of U.S. Counterterrorism Strategy,” public remarks delivered at the Wilson 

Center, April 30, 2012, available from www.wilsoncenter.org/event/

the-efficacy-and-ethics-us-counterterrorism-strategy.
143

129. John Yoo, “Assassinations or Targeted Killings since 

9/11,” New York Law School Review, Vol. 57, 2011, p. 63. 

130. According to recent polling, while Americans generally are concerned over government surveillance, some 82 percent say it is acceptable to monitor communications of suspected 

terrorists. For details of recent polling, see Lee Rainie and Mary 

Maddens, “Americans’ Privacy Strategies Post-Snowden,” Pew 

Research Center, March 16, 2015, available from www.pewinternet.org/2015/03/16/americans-privacy-strategies-post-snowden/. For 

public opinion on drone strikes, see Sarah Kreps, “Do Americans 

Really Love Drone Strikes?” The Washington Post, June 6, 2014. 

131. George W. Bush, Homeland Security Presidential Directive

(HSPD-6, Directive on Integration and Use of Screening Information 

To Protect Against Terrorism), September 16, 2003, available from 

www.gpo.gov/fdsys/pkg/PPP-2003-book2/pdf/PPP-2003-book2-docpg1174.pdf.

132. George W. Bush, Directive on Biometrics for Identification 

and Screening to Enhance National Security, June 5, 2008, available 

from www.gpo.gov/fdsys/pkg/PPP-2008-book1/pdf/PPP-2008-book1-

doc-pg757.pdf. Also see DoD Instruction on Defense Biometric and 

Forensic Enabled Intelligence (DoD O-3300.bb), directing DoD to 

make available to other U.S. Government agencies, to the fullest extent permitted by law, all biometric and associated information on persons posing a threat to national security. Biometric 

data collected by DoD is integrated into the Automated Biometric 

Identification System (ABIS) and shared with the Department of 

Homeland Security, FBI, and various elements of the intelligence 

community.

133. Dennis C. Blair, Senate Select Committee on Intelligence, 

U.S. Intelligence Community Annual Threat Assessment 2010, February 3, 2010, available from www.dni.gov/files/documents/Newsroom/

Testimonies/20100203_testimony.pdf.

134. Steve Coll, “The Unblinking Stare: The Drone War in 

Pakistan,” The New Yorker, November 24, 2014.

135. One recent notable example was the January 2015 drone 

strike that accidentally killed two hostages held by al-Qaeda, one 
144

of them an American. Similar strikes also killed two Americans 

who were reportedly members of al-Qaeda, Ahmed Farouq and 

Adam Gadahn, who were not specifically targeted as part of the 

operation. See Peter Baker and Julie Hirschfeld Davis, “2 Qaeda 

Hostages Were Accidentally Killed in U.S. Strike, White House 

Says,” The New York Times, April 23, 2015, available from www.

nytimes.com/2015/04/24/world/asia/2-qaeda-hostages-were-accidentally-killed-in-us-raid-white-house-says.html?hp&action=click&pgty

pe=Homepage&module=span-ab-top-region®ion=top-news&WT.

nav=top-news.

136. Patrick Tucker, “How Special Operators Are Taking 

Artificial Intelligence To War,” Defense One, May 28, 2015, available from www.defenseone.com/technology/2015/05/how-special-operators-are-taking-artificial-intelligence-war/113872/?oref=defenseone_

today_nl.

137. Danya Greenfield, “The Case Against Drone Strikes on 

People Who Only ‘Act’ Like Terrorists,” The Atlantic, August 19, 

2013. Also see Civilian Casualties & Collateral Damage at the Brookings Institute Lawfare Blog, available from www.lawfareblog.com/

wiki/the-lawfare-wiki-document-library/targeted-killing/controversy.

138. Greg Miller, “Plan for Hunting Terrorists Signals U.S. 

Intends to Keep Adding Names to Kill Lists,” Washington Post, 

October 23, 2012.

139. Comment by Micah Zenko, a scholar at the Council on 

Foreign Relations and the lead author of a 2013 study of drone 

warfare. See Scott Shane, “Drone Strikes Reveal Uncomfortable 

Truth: U.S. is Often Unsure About Who Will Die,” The New York 

Times, April 23, 2015, available from www.nyti.ms/1JiGHtY.

140. See Brennan, “The Efficacy and Ethics of U.S. Counterterrorism Strategy.” Also, one recent example of this policy was 

the capture of a “top al-Qaeda operative” and American citizen, 

Muhanad Mahmoud al Farekh, in Pakistan, who was reportedly 

nominated to the Pentagon’s “kill list” of suspected terrorists in 

2013; however, he was captured by Pakistani forces and secretly flown to the United States to face federal terrorism charges. 

See Adam Goldman and Tim Craig, “American Citizen Linked 

to al-Qaeda is Captured, Flown Secretly to U.S.,” The Washington Post, April 2, 2015, available from www.washingtonpost.com/
145

world/national-security/american-citizen-suspected-of-being-al-qaeda-member-captured-brought-to-us/2015/04/02/48e8cc4c-d89c-11e4-

8103-fa84725dbf9d_story.html.

141. Christopher Lamb and Evan Munsing, Secret Weapon: 

High-Value Target Teams as an Organizational Innovation, Washington, DC: Institute for National Strategic Studies, National Defense 

University, 2011, p. 53.

142. Tom Peter, “In Iraq, Troops Balance Fighting and Lending a Hand,” The Christian Science Monitor, August 7, 2008, available from www.csmonitor.com/World/Middle-East/2008/0807/p06s

01-wome.html.

143. Gabriella Blum, “The Individualization of War: From War 

to Policing in the Regulation of Armed Conflicts,” Sarat, Douglas, 

and Umphrey, eds., Law and War, Stanford, CA: Stanford University Press, 2013, available from www.ssrn.com/abstract=2231168.

144. For a useful discussion of World War II as a “scientists’ 

war,” see Paul Kennedy, Engineers of Victory: The Problem Solvers 

who Turned the Tide in the Second World War, New York: Random 

House, 2013.

145. See articles on personality identification playing cards, 

available from www.defenselink.mil/news/Apr2003/pipc10042003.

html; and Tom Zucco, “Troops Dealt an Old Tool,” St. Petersburg 

Times, April 12, 2003, available from www.sptimes.com/2003/04/12/

Worldandnation/Troops_dealt_an_old_t.shtml.

146. Sydney J. Freedberg, “Raiders, Advisors and the Wrong 

Lessons from Iraq,” Breaking Defense, March 20, 2013, available from 

www.breakingdefense.com/2013/03/gen-mcmaster-raiders-advisorsand-the-wrong-lessons-from-iraq.

147. Barrack Obama, “Remarks by the President at the National Defense University, Speech at the National Defense University, May 23, 2013, transcript available from www.whitehouse.

gov/the-press-office/2013/05/23/remarks-president-national-defenseuniversity.
146

148. Adam Grissom, “The Future of Military Innovation 

Studies,” The Journal of Strategic Studies, Vol. 29, No. 5, October 

2006, p. 907.

149. For one perspective on this distinction, see Stephen 

Rosen, Winning the Next War: Innovation and the Modern Military, 

Ithica, NY: Cornell University Press, 1991.

150. President Barack Obama, Interview on Fareed Zakaria 

GPS, February 1, 2015, transcript available from transcripts.cnn.

com/TRANSCRIPTS/1502/01/fzgps.01.html.

151. Patrick Tucker, “What Happens When Spies Can Eavesdrop on Any Conversation?” Defense One, December 1, 2014, 

available from www.defenseone.com/technology/2014/12/whathappens-when-spies-can-eavesdrop-any-conversation/100142/?oref=

defenseone_today_nl.

152. Jim Garamone, “Capabilities Must Match Future Threats, 

Army Leader Says,” DoD News, Defense Media Activity, February 24, 2015, available from www.defense.gov/news/newsarticle.

aspx?id=128237.

153. U.S. National Intelligence Council, Global Trends 2030: Alternative Worlds, Washington, DC: U.S. Director of National Intelligence, December 2012, pp. 59–60.

154. Matthew G. Olsen, Director of the National Counterterrorism Center, Hearing before the Senate Committee on 

Homeland Security, “Worldwide Threats to the Homeland,” 

September 17, 2014, available from www.nctc.gov/docs/2014_worldwide_threats_to_the_homeland.pdf.

155. Howard Altman, “Experience Briefing President Leads 

New Agency Chief to Raise Bar,” The Tampa Tribune, November 

14, 2014, available from www.tbo.com/list/military-news/experiencebriefing-president-leads-new-agency-chief-to-raise-bar-20141114.

156. Global Trends 2030, p. 50.

157. Ken Dilanian, “20,000 Foreign Fighters Flock to Syria, 

Iraq,” Associated Press, February 10, 2015, available from news.
147

yahoo.com/ap-exclusive-20-000-foreign-fighters-flock-syria-211811073.

html.

158. Eric Schmitt and Michael Schmidt, “West Struggles to 

Halt Flow of Citizens to War Zones,” The New York Times, January 

13, 2015, available from www.nytimes.com/2015/01/13/world/weststruggles-against-flow-to-war-zones.html.

159. “Report on Foreign Terrorist Fighters,” New York: 

United Nations, March 2015, pp. 14-23.

160. Ibid., pp. 18-23.

161. Greg Miller, “Backlash in Berlin Over NSA Spying Recedes as Threat from Islamic State Rises,” The Washington Post, 

December 29, 2014, available from www.washingtonpost.com/world/

national-security/backlash-in-berlin-over-nsa-recedes-as-islamic-staterises/2014/12/29/c738af28-8aad-11e4-a085-34e9b9f09a58_story.html.

162. Ahmed Rashid, “Waking Up to the New al-Qaeda,” New 

York Review of Books, January 12, 2015, available from www.nybooks.com/blogs/nyrblog/2015/jan/12/paris-attacks-waking-al-qaeda.

163. John Mueller and Mark Stewart, “How French Intelligence Missed the Charlie Hebdo Terrorists,” Time, January 14, 

2015, available from www.time.com/author/john-mueller-and-markstewart.

164. Alissa J. Rubin, “Lawmakers in France Move to Vastly 

Expand Surveillance,” The New York Times, May 5, 2015, available 

from www.nytimes.com/2015/05/06/world/europe/french-legislatorsapprove-sweeping-intelligence-bill.html?_r=0.

165. An expression coined by NATO officials and first used 

during the 2014 Crimean crisis, referring to masked Russian soldiers and military equipment within Ukraine.

166. Assessment by the International Institute for Strategic 

Studies (IISS) in the annual Military Balance report, see Radio Free 

Europe/Radio Liberty, “Report Warns Russia’s Hybrid Warfare 

In Ukraine Could Inspire Others,” February 11, 2015, available 

from www.rferl.org/content/russia-hybrid-warfare-ukraine-could-inspire-others/26842566.html.
148

167. Agence France-Press, “French Soldiers In Mali Stalked 

By Invisible Enemy,” May 30, 2015, available from www.defnews.

ly/1HCvUFn.

168. Patrick Tucker, “What Happens When Spies Can Eavesdrop on Any Conversation?” Defense One, December 1, 2014, available from www.defenseone.com/technology/2014/12/whathappens-when-spies-can-eavesdrop-any-conversation/100142/.

169. Elizabeth Young, “Summary of the “Decade of War,” 

Prism, Vol. 4, No. 2, 2013, p. 126.

170. Alan Gelb and Julia Clark, “Identification for Development: The Biometrics Revolution,” Washington, DC: Center for 

Global Development, January 2013, p. 7.

171. “UN begins biometric registration of Rwandans in DRC,” 

Planet Biometrics, April 13, 2015, available from www.planetbiometrics.com/article-details/i/2905/.

172. Justin Lee, “UNHCR, Accenture Provide Global Biometric Identity Management System to Help Refugees,” Biometric Update, May 25, 2015, available from www.biometricupdate.

com/201505/unhcr-accenture-provide-global-biometric-identity-management-system-to-help-refugees.

173. Jennifer Hicks, “United Nations High Commissioner For 

Refugees Adopts Biometric Tracking,” Forbes, May 31, 2015, available from www.forbes.com/sites/jenniferhicks/2015/05/30/unitednations-high-commissioner-for-refugees-adopts-biometric-tracking/.

174. Rosa Brooks, “Know Thy Enemy, and the Future of 

Memorial Day,” Foreign Policy, May 25, 2015, available from 

www.foreignpolicy.com/2015/05/25/drones-dna-facebook-future-warmemorial-day/.

175. Charles J. Dunlap, Jr., “The Hyper-Personalization of 

War: Cyber, Big Data, and the Changing Face of Conflict,” Georgetown Journal of International Affairs, Vol. 15, 2014, pp. 108-118.

176. Estimate based on a recent technology market research 

report by ABI Research. See Anna Forrester, “Commercial Segment to Overtake Government in Biometrics Spending by 2017,” 
149

ExecutiveBiz Blog, February 11, 2015, available from www.blog.executivebiz.com/2015/02/abi-research-commercial-segment-to-overtakegovt-in-biometrics-spending-by-2017/.

177. Justin Lee, “U.S. Government Biometrics Spend to Reach 

US$8.6 B by 2020: ABI Research,” Biometrics Update, April 20, 2015, 

available from www.biometricupdate.com/201504/u-s-governmentbiometrics-spend-to-reach-us8-6-b-by-2020-abi-research. 

178. Zack Martin, “Goode Predicts Mobile Biometric Growth,” 

Secure ID News, February 4, 2014, available from www.secureidnews.com/news-item/goode-predicts-mobile-biometric-growth/#. Article references the monthly industry report by Goode Intelligence 

entitled, “Fingerprint Biometrics and Mobile and Wearable Biometrics.”

179. Gelb and Clark.

180. “Government and technology: Playing leapfrog: The 

Wonders of Smart Systems,” The Economist, May 24, 2015, available from www.economist.com/node/21651330/print.

181. UK Home Office, Guidance Notes, “Biometric Residence 

Permits, General Information for Applicants, Employers and 

Sponsors,” March 2015, available from www.gov.uk/government/

uploads/system/uploads/attachment_data/file/413959/v_4_BRP_-_Generic_information_leaflet_2_March_-_6_April__clean_.pdf.

182. “Nigerian Communications Commission Deploys BIOKey Fingerprint Technology for National SIM Card Registration 

Program,” Marketwired, May 28, 2013, available from www.finance.yahoo.com/news/nigerian-communications-commission-deploysbio-120000546.html.

183. Tim Craig and Shaiq Hussain, “Pakistanis Face a Deadline: 

Surrender Fingerprints or Give up Cellphone,” The Washington 

Post, February 23, 2015, available from www.washingtonpost.

com/world/asia_pacific/pakistanis-face-a-deadline-surrender-fingerprints-or-give-up-cellphone/2015/02/23/de995

a88-b932-11e4-bc30-a4e75503948a_story.html?utm_

source=Sailthru&utm_medium=email&utm_term=%2ASituation%20

Report&utm_campaign=Sit%20Rep%20February%2025%202015.
150

184. Paul Roderick Gregory, “Putin’s New Weapon In The 

Ukraine Propaganda War: Internet Trolls,” Forbes, December 

9, 2014, available from www.forbes.com/sites/paulroderickgregory/2014/12/09/putins-new-weapon-in-the-ukraine-propaganda-warinternet-trolls/.

185. Department of Defense Cyber Strategy, Washington, DC: 

U.S. Government Printing Office, April 2015, p. 12.

186. Linda Kinstler, “Global Cyber Defense Demand Will 

Exceed Capability for Years To Come,” Defense One, January 

28, 2015, available from www.defenseone.com/technology/2015/01/

global-cyber-defense-demand-will-exceed-capability-yearscome/103983/.

187. Chris White, “New Search Engine Exposes the Dark 

Web,” Interview with CBS 60 Minutes with the DARPA developer on a new technology for searching internet “dark web” sites, 

February 8, 2015, available from www.cbsnews.com/news/newsearch-engine-exposes-the-dark-web.

188. Tor, or The Onion Routing project, was originally developed by the U.S. Naval Research Laboratory for the purpose of 

protecting government communications and is now used by a 

wide variety of private and commercial interests. For a discussion 

of the potential for anonymous use of Bitcoin, see Craig Elwell, 

Maureen Murphy, and Michael Seitzinger, Bitcoin: Questions, Answers, and Analysis of Legal Issues, Washington, DC: Congressional 

Research Service, January 28, 2015, p. 3.

189. Mark Wallace, Written Testimony before the House Committee on Foreign Affairs Subcommittee on Terrorism, Nonproliferation, and Trade, “The Evolution of Terrorist Propaganda: The 

Paris Attack and Social Media,” January 27, 2015, available from 

www.counterextremism.com/press/counter-extremism-project-ceomark-wallace-testify-house-committee-foreign-affairs.

190. Tim Fernholz, “Terrorism Finance Trackers Worry ISIS 

Already Using Bitcoin,” February 13, 2015, Defense One, available 

from www.defenseone.com/threats/2015/02/terrorism-finance-trackersworry-isis-already-using-bitcoin/105345/?oref=defenseone_today_nl.
151

191. Patrick Tucker, “How the Military Will Fight ISIS on the 

Dark Web,” February 24, 2015, Defense One, available from www.

defenseone.com/technology/2015/02/how-military-will-fight-isis-darkweb/105948/?oref=defenseone_today_nl.

192. National Intelligence Council, Global Trends 2030: Alternative Worlds, Washington, DC: National Intelligence Council, December 2012, p. 128, available from www.dni.gov/files/documents/

GlobalTrends_2030.pdf.

193. Matthew G. Olsen, Hearing before the Senate Committee 

on Homeland Security, “Worldwide Threats to the Homeland,” 

September 17, 2014.

194. Gabriel Weimann, New Terrorism and New Media, Washington, DC: Commons Lab of the Woodrow Wilson International 

Center for Scholars, 2014, p. 1.

195. Michael Chertoff and Tobby Simon, “The Impact of the 

Dark Web on Internet Governance and Cyber Security: Global 

Commission on Internet Governance Paper Series No. 6,” Waterloo, Ontario, Canada: Centre for International Governance Innovation and the Royal Institute for International Affairs, February 

2015, p. 1.

196. Patrick Tucker, “What Your Facebook Posts Mean to U.S. 

Special Operations Forces,” Defense One, January 29, 2015, available from www.defenseone.com/technology/2015/01/what-your-facebook-posts-mean-usspecialforces/104031/?oref=defenseone_today_nl.

197. Marc Lynch, Deen Freelon, and Sean Aday, “Syria’s 

Socially Mediated Civil War,” Peaceworks, No. 91, January 2014, 

available from www.usip.org/sites/default/files/PW91-Syrias%20Socially%20Mediated%20Civil%20War.pdf.

198. Weimann, p. 2; and Haroro Ingram, “Three Traits of the 

Islamic State’s Information Warfare,” The RUSI Journal, Vol. 159, 

No. 6, 2004, pp. 4-11.

199. J. M Berger and Jonathon Morgan, The ISIS Twitter Census: 

Defining and Describing the Population of ISIS Supporters on Twitter, Analysis Paper No. 20, Washington, DC: Brookings Project 
152

on U.S. Relations with the Islamic World, March 2015, p. 2. Also 

see Brian Bennett, “With Islamic State Using Instant Messaging 

Apps, FBI Seeks Access to Data,” The Los Angeles Times, June 10, 

2015, available from www.latimes.com/world/middleeast/la-fg-terrormessaging-20150608-story.html#page=1 1/5.

200. Joseph Carter, Shiraz Maher, and Peter Neumann, 

#Greenbirds: Measuring the Importance and Influence of Foreign 

Fighter Networks, London, UK: The International Centre for the 

Study of Radicalization and Political Violence, April 2014, available from www.icsr.info/wp-content/uploads/2014/04/ICSR- ReportGreenbirds-Measuring-Importance-and-Infleunce-in-Syrian-ForeignFighter-Networks.pdf.

201. Joe Parkinson and Maria Abi-Habib, “How Jihadists Slip 

Through Europe’s Dragnet and Into Syria,” The Wall Street Journal, February 20, 2015, available from www.wsj.com/articles/howjihadists-slip-through-europes-dragnet-and-into-syria-1424653670.

202. Julian Barnes, “U.S. Military Plugs into Social Media for 

Intelligence Gathering,” The Wall Street Journal, August 6, 2014, 

available from www.wsj.com/articles/u-s-military-plugs-into-socialmedia-for-intelligence-gathering-1407346557.

203. James Rupert, “Russian Troops Lead Moscow’s Biggest 

Direct Offensive in Ukraine Since August,” January 23, 2015, Atlantic Council, available from www.atlanticcouncil.org/blogs/newatlanticist/russian-special-forces-and-regular-troops-lead-moscow-sbiggest-direct-offensive-in-ukraine-since-august.

204. Daniel Regalado, Nart Villeneuve, and John Scott Railton, Behind the Syrian Conflict’s Digital Front Lines, Milpitas, CA: 

Firefly Inc., February 2015, p. 5.

205. Behind the Syrian Conflict’s Digital Front Lines, p. 18.

206. Brian Everstine, “Carlisle: Air Force Intel Uses ISIS ‘Moron’s’ Social Media Posts to Target Airstrikes,” Air Force Times, 

June 4, 2015, available from www.airforcetimes.com/story/military/

tech/2015/06/04/air-force-isis-social-media-target/28473723/.

207. See “The ISIS Twitter Census,” as one recent example.
153

208. “Anonymous ‘Hacktivists’ Strike a Blow against ISIS,” 

Anonymous website, available from www.anonhq.com/anonymoushacktivists-strike-blow-isis. 

209. David Sanger and Eric Schmitt, “Hackers Use Old Lure 

on Web to Help Syrian Government,” The New York Times, February 1, 2015, available from www.nytimes.com/2015/02/02/world/middleeast/hackers-use-old-web-lure-to-aid-assad.html?_r=0.

210. Generically, “OPSEC” refers to a process that identifies 

one’s own critical information and determines if it can be obtained by adversaries and exploited for their use. It also includes 

the use of measures that eliminate or reduce these risks.

211. Johann Wolfgang von Goethe, Maxims and Reflections, 

Bailey Saunders, trans., New York: The MacMillan Company, 

1906, available from onlinebooks.library.upenn.edu/webbin/gutbook/

lookup?num=33670.

212. Charles J. Dunlap, Jr., “The Hyper-Personalization of 

War: Cyber, Big Data, and the Changing Face of Conflict,” Vol. 

15, Georgetown Journal of International Affairs, 2014, p. 113.

213. Michael Shear and Scott Shane, “White House Weighs 

Sanctions after Second Breach of a Computer System,” The 

New York Times, June 12, 2015, available from www.nytimes.

com/2015/06/13/us/white-house-weighs-sanctions-after-second-breachof-a-computer-system.html?hp&action=click&pgtype=Homepage&

module=first-column-region&_r=0.

214. Patrick Radden Keeke, “Rocket Man: How an Unemployed Blogger Confirmed that Syria had Used Chemical Weapons,” The New Yorker, November 25, 2013, available from www.

newyorker.com/magazine/2013/11/25/rocket-man-2.

215. The same individual involved in the Syrian chemical 

weapons reporting also founded Bellingcat, a forum for citizen 

investigative journalism with a focus on military and security 

issues. See www.bellingcat.com.

216. Paul Rosenzweig, Cyberwarfare: How Conflicts in Cyberspace are Challenging America and Changing the World, Santa 

Barbara, CA: ABC-CLIO, 2013, p. 20.
154

217. Weimann, p. 10.

218. Patrick Tucker, “What Happens When You Pose as the 

Defense Secretary on Twitter?” Defense One, December 2, 2014, 

available from www.defenseone.com/technology/2014/12/what-hap

pens-when-you-pose-next-defense-secretary-twitter/100304/.

219. Bruce Schneier, “Hacker or Spy? In Today’s Cyberattacks, Finding the Culprit is a Troubling Puzzle,” The Christian 

Science Monitor, March 4, 2015, available from www.csmonitor.com/

World/Passcode/Passcode-Voices/2015/0304/Hacker-or-spy-In-today-scyberattacks-finding-the-culprit-is-a-troubling-puzzle.

220. Greg Miller, “CIA Looks to Expand its Cyber Espionage Capabilities,” The Washington Post, February 23, 2015, 

available from www.washingtonpost.com/world/national-security/

cia-looks-to-expand-its-cyber-espionage-capabilities/2015/02/23/

a028e80c-b94d-11e4-9423-f3d0a1ec335c_story.html?utm_

source=Sailthru&utm_medium=email&utm_term=%2ASituation%20

Report&utm_campaign=Sit%20Rep%20February%2025%202015.

221. Carl von Clausewitz, On War, Michael Howard and Peter 

Paret, eds. and trans., Princeton, NJ: Princeton University Press, 

1976, p. 595.

222. For doctrinal discussion on this issue, see JP 5-0, Joint 

Operation Planning, Chap. III, “Operational Art and Operational 

Design,” August 11, 2011.

223. Mary Madden and Lee Rainie, “Americans’ Attitudes 

about Privacy, Security, and Surveillance,” Pew Research Center, 

May 20, 2015, available from: www.pewinternet.org/2015 /05/20/

americans-attitudes-about-privacy-security-and-surveillance/.

224. Ibid. Some 69 percent of adults say they are not confident 

that records of their activity maintained by the social media sites 

they use will remain private and secure, while only 9 percent say 

they feel they have “a lot” of control over how much information 

is collected about them and how it is used.

225. Sydney Freedberg, “6 Threats, 6 Changes, & A Brave 

New World: Intel Chief Vickers,” Breaking Defense, January 21, 

2015, available from www.breakingdefense.com/2015/01/6-threats6-changes-a-brave-new-world-intel-chief-vickers.
155

226. Brian Barrett, “Can Google’s Future-Lab Finally Kill 

the Password?” Wired, May 29, 2015, available from www.wired.

com/2015/05/google-atap-passwords-vault-io/.

227. Evelyn Brown, Performance of Facial Recognition Software 

Continues to Improve, Washington, DC: National Institute of Standards and Technology, June 3, 2014, available from www.nist.gov/

itl/iad/face-060314.cfm. Data based on a research study conducted 

by Patrick Grother and Mei Ngan, “Performance of Face identification Algorithms.”

228. Andrea Peterson, “The Biometrics Revolution is Already 

Here—and You May Not be Ready For It,” The Washington Post, 

October 17, 2014, available from www.washingtonpost.com/blogs/

the-switch/wp/2014/10/17/the-biometrics-revolution-is-already-hereand-you-may-not-be-ready-for-it.

229. Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, 

and Lior Wolf, “DeepFace: Closing the Gap to Human-Level 

Performance in Face Verification,” Facebook Research 

Publication, Menlo Park, CA and Tel Aviv University, Tel 

Aviv, Israel, 2014, available from https://research.facebook.com/

publications/480567225376225/deepface-closing-the-gap-to-human-level-performance-in-face-verification/. Also see Tom Simonite, “Facebook Creates Software That Matches Faces Almost as Well as You Do,” MIT Technology Review, March 

17, 2014, available from www.technologyreview.com/news/

525586/facebook-creates-software-that-matches-faces-almost-aswell-as-you-do/, accessed January 7, 2015.

230. National Science and Technology Council (NSTC) Committee on Homeland and National Security, Subcommittee on 

Biometrics, Introduction to Biometrics, pp. 129-130, available from 

www.biometrics.gov/ReferenceRoom/Introduction.aspx.

231. Major distorting factors are referenced by the acronym, 

APIER—aging, pose, illumination, expression, and resolution.

232. Patrick Grother and Mei Ngan, “Face Recognition Vendor Test: Performance of Face Identification Algorithms,” Washington, DC: Department of Commerce, National Institute of Standards and Technology, May 26, 2014, available from www.nist.

gov/customcf/get_pdf.cfm?pub_id=915761.
156

233. Stephan Mracek, Jan Vana, Radim Dvorak, and Svetlana 

Yanushkevich, “3D and Thermo-Face Fusion,” Jucheng Yang and 

Shan Juan Xie, eds., New Trends and Developments in Biometrics, 

Intech, November 28, 2012, available from www.intechopen.com/

books/new-trends-and-developments-in-biometrics.

234. Erik Sofge, “The End of Anonymity,” Popular Science, 

January 15, 2014, available from www.popsci.com/article/technology/

end-anonymity.

235. While not exclusively useful for identification purposes, 

physiological characteristics and thermal markers might be employed for screening individuals suspected of having certain 

medical issues, fever, etc.

236. Babak Hodjat, “Myth Busting Artificial Intelligence,” 

Wired Magazine, February 19, 2015, available from www.wired.com/

insights/2015/02/myth-busting-artificial-intelligence/.

237 . Robert Hof, “Deep Learning,” MIT Technology Review, 

April 23, 2013, available from www.technologyreview.com/featuredstory/513696/deep-learning.

238 . Quentin Hardy, “Facebook Offers Artificial Intelligence 

Tech to Open Source Group,” January 16, 2015, The New York 

Times, available from bits.blogs.nytimes.com/2015/01/16/facebookoffers-artificial-intelligence-tech-to-open-source-group/.

239. Jose Pagliery, “FBI Launches a Face Recognition System,” CNN.com, September 16, 2014, available from www.money.

cnn.com/2014/09/16/technology/security/fbi-facial-recognition.

240. “The Face Detection Algorithm Set To Revolutionize Image Search,” MIT Technology Review, February 16, 2015, referencing the paper by Sachin Sudhakar Farfade, Mohammad Saberian, 

and Li-Jia Li, “Multi-view Face Detection Using Deep Convolutional Neural Networks,” February 10, 2015, available from www.

arxiv.org/abs/1502.02766.

241. James Albers, “The Truth about Biometric Exit Technology,” Security Today, October 1, 2014, available from www.

security-today.com/articles/2014/10/01/the-truth-about-biometric-exit.
157

aspx. StereoVision recently demonstrated a Wireless 3D Binocular 

Face Recognition System in response to a Navy contract for technologies focused on “stand-off identification of uncooperative 

subjects.” See Patrick Tucker, “The Navy’s New Binoculars Can 

Identify You From 700 Feet Away,” Defense One, May 15, 2015.

242. Alex Hern, “Hacker Fakes German Minister’s Fingerprints,” The Guardian, December 30, 2014, available from www.theguardian.com/technology/2014/dec/30/hacker-fakes-german-ministersfingerprints-using-photos-of-her-hands.

243. Peter Counter, “New Iris Innovations May Bring EyeBased Biometrics to the Masses,” Mobile ID World, January 15, 

2015, available from www. mobileidworld.com/new-iris-innovationsmay-bring-eye-based-biometrics-to-the-masses-1151.

244. Sean Lyngaas, “Can the Pentagon Keep Pace on Biometrics?” FCW.com, March 11, 2015, available from fcw.com/

Articles/2015/03/11/Can-the-Pentagon-keep-pace-on-biometrics.

aspx?m=2&p=1.

245. Ibid.

246. Federal Bureau of Investigation, “Image-Based Matching 

Technology Offers Identification and Intelligence Prospects,” FBI 

Press Release, December 2012, available from https://www.fbi.gov/

about-us/cjis/cjis-link/december-2012/Image-Based%20Matching%20

Technology%20Offers%20Identification%20and%20Intelligence%20

Prospects.

247. Douglas Reynolds, “Speaker Verification: From Research 

to Reality,” Paper presented at the 2002 Institute of Electrical and 

Electronics Engineers (IEEE), International Conference Acoustics, 

Speech, and Signal Processing, May 13-17, 2002, available from 

www.ll.mit.edu/mission/cybersec/publications/publication-files/full_

papers/020513_Reynolds.pdf.

248. Taylor Haney, The Future of Voiceprints, Los Angeles, 

CA: Annenberg Media Center, December 27, 2014, available from 

www.neontommy.com/news/2014/12/future-voiceprints.

249. Patrick Tucker, “What Happens When Spies Can Eavesdrop on Any Conversation?” Defense One, December 1, 2014, 
158

available from www.defenseone.com/technology/2014/12/what-happens-when-spies-can-eavesdrop-any-conversation/100142/.

250. Homayoon Beigi, “Speaker Recognition: Advancements 

and Challenges,” Jucheng Yang and Shan Juan Xie, eds., New 

Trends and Developments in Biometrics, Intech, November 28, 2012, 

available from www.intechopen.com/books/new-trends-and-developments-in-biometrics.

251. NSTC, Committee on Homeland and National Security, Subcommittee on Biometrics, Introduction to Biometrics, 

pp. 129-130, available from www.biometrics.gov/ReferenceRoom/

Introduction.aspx.

252. VIBES Product Information Sheet from the Leidos Corporation, Reston, VA, available from www.leidos.com/sites/default/

files/files/14-0439vFc-VIBES_FS.pdf.

253. Rachel Metz, “Deep Learning Squeezed onto a Phone,” 

MIT Technology Review, February 9, 2015, available from www.

technologyreview.com/news/534736/deep-learning-squeezed-onto-aphone.

254. Ray Locker, “Military Could Be Using High-Tech 

Speech Software by 2017,” USA Today, February 22, 2015, 

available from www.usatoday.com/story/news/nation/militaryintel

ligence/2015/02/22/robust-speech-translation-transcription-software/23839041/.

255. “Laboratory Team Takes Honors at the 2014 Audio/Visual Emotion Challenge and Workshop,” MIT Lincoln Laboratory, 

The Bulletin, January 9, 2015.

256. Chuck Brooks, “Human Factors and Biometrics at DHS,” 

Biometric Update, August 1, 2014, available from www.biometricupdate.com/201408/human-factors-and-biometrics-at-dhs.

257. DARPA, Detection & Computational Analysis of

Psychological Signals Information Sheet, May 2013, available from www. webcache.googleusercontent.com/search?q=cache:

mM_FJrE2m7oJ:www.darpa.mil/opencatalog/DCAPShtml+&cd=

2&hl=en&ct=clnk&gl=us.
159

258. Norberto Andradejun, “Computers Are Getting Better 

Than Humans at Facial Recognition,” The Atlantic, June 9, 2014, 

available from www.theatlantic.com/technology/archive/2014/06/

bad-news-computers-are-getting-better-than-we-are-at-facialrecognition/372377.

259. Roman Yampolskiy and Venu Govindaraju, “Behavioral 

Biometrics: A Survey and Classification,” International Journal of 

Biometrics, Vol. 1, No. 1, 2008, p. 81.

260. Ibid., p. 88.

261. Julian Barnes, “U.S. Military Plugs into Social Media for 

Intelligence Gathering, Defense Intelligence Agency Head Says 

Online Postings Played Crucial Role in Ukraine Jet Shootdown,” 

The Wall Street Journal, August 6, 2014, available from www.wsj.

com/articles/u-s-military-plugs-into-social-media-for-intelligence-gathering-1407346557.

262. Peter Counter, “Behavioral Biometrics to Become Increasingly Standard in Fraud Detection,” FindBiometrics, July 23, 

2014, available from www.findbiometrics.com/behavioural-biometricsto-become-increasingly-standard-in-fraud-detection.

263. Boer Deng, “People Identified through Credit-Card Use 

Alone,” Nature, January 29, 2015, available from www.nature.com/

news/people-identified-through-credit-card-use-alone-1.16817.

264. For one recent example of these applications, see patent filing for Israeli behavioral biometrics firm BioCatch. Related 

article available from “BioCatch Granted Behavioural Biometric 

Patent for Mobiles,” Planet Biometrics, February 25, 2015, available 

from www.planetbiometrics.com/article-details/i/2746.

265. James Vincent, “Behaviosec uses Behavioral Biometrics 

to Find If the Person Using a Mobile Device Is Really Who They 

Claim to Be,” The Independent, September 2, 2014, available from 

www.economictimes.indiatimes.com/news/international/business/

behaviosec-uses-behavioural-biometrics-to-find-if-the-person-using-a-mobile-device-is-really-who-they-claim-to-be/articleshow/

41463585.cms.
160

266. Aliya Sternstein, “NSA Trying to Track Your Smartphone Finger Strokes,” Defense One, May 26, 2015, available 

from www.defenseone.com/technology/2015/05/nsa-trying-track-yoursmartphone-finger-strokes/113638/??oref=d_brief_nl.

267. Yedid Hoshen and Shmuel Peleg, “Egocentric Video Biometrics,” Jerusalem, Israel: The Hebrew University of Jerusalem, 

November 27, 2014, available from Cornell University Library at 

www.arxiv.org/pdf/1411.7591v1.pdf. 

268. Rachel Metz, “Deep Learning Squeezed onto a Phone,” 

MIT Technology Review, February 9, 2015, available from www.

technologyreview.com/news/534736/deep-learning-squeezed-onto-aphone/.

269. Yampolskiy and Govindaraju, p. 93.

270. D. A. Reid, S. Samangooei, C. Chen, M. S. Nixon, and 

A. Ross, “Soft Biometrics for Surveillance: An Overview,” C. R. 

Rao and Venu Govindaraju, eds., Handbook of Statistics, Vol. 31, 

Oxford, UK: Elsevier, 2013.

271. Daniel Reid and Mark Nixon, “Imputing Human Descriptions in Semantic Biometrics,” paper presented at the Multimedia 

in Forensics, Security and Intelligence conference, Florence, Italy, 

October 2010, available from eprints.soton.ac.uk/271623/1/mifor541-

reid.pdf.

272. J. Thornton, J. Baran-Gale, D. Butler, M. Chan, and H. 

Zwahlen, “Person Attribute Search For Large-Area Video Surveillance,” IEEE Conference on Technologies for Homeland 

Security, 2011, available from ieeexplore.ieee.org/xpl/login.jsp?

tp=&arnumber=6107847&url=http%3A%2F%2Fieeexplore.ieee.

org%2Fstamp%2Fstamp.jsp%3Ftp%3D%26arnumber%3D6107847.

273. The probability that two unrelated individuals would 

share all 13 pairs of alleles used in the FBI’s n 13 core short tandem repeat (STR) profile is estimated to be one in several hundred billion. See Nathan James, “DNA Testing in Criminal Justice: 

Background, Current Law, Grants, and Issues,” Washington, DC: 

Congressional Research Service, December 6, 2012, p. 3.
161

274. Another method involves the use of mitochondrial DNA 

(mtDNA) sequencing that is useful for evidence samples such as 

hair fragments, bones, and teeth. This analysis is highly sensitive 

and therefore can be used in cases with limited biological material 

or degraded samples. However, because mtDNA profiles are only 

shared between maternal family members, this method is not as 

discriminating as STR analysis for identification.

275. Lisa Gefrides and Katie Welch, “Forensic Biology: Serology and DNA,” Ashraf Mozayani and Carla Noziglia, eds., The 

Forensic Laboratory Handbook Procedures and Practice, New York: 

Springer, 2011, p. 28.

276. DNA Casework Unit (DCU) Information Page, FBI website, available from www.fbi.gov/about-us/lab/biometric-analysis/dnacasework-unit-dcu-1.

277. FBI Rapid DNA or Rapid DNA Analysis, FBI homepage, 

available from www.fbi.gov/about-us/lab/biometric-analysis/codis.

278. Kathy Pretz, “New System to Speed Up DNA Analysis,” The Institute, February 4, 2013, available from theinstitute.ieee.

org/technology-focus/technology-topic/new-system-to-speed-up-dnaanalysis. For a technical description, see Melissa May, “Field-Deployable Rapid DNA Analysis: Fully Integrated, Fully Automated Generation of Short Tandem Repeat Profiles of Buccal Swabs,” 

IEEE Conference on Technologies for Homeland Security, 2011.

279. Patrick Tucker, “Special Operators are Using Rapid 

DNA Readers,” Defense One, May 21, 2015, available from www.

defenseone.com/technology/2015/05/special-operators-are-using-rapiddna-readers/113383/?oref=defenseone_today_nl.

280. Gefrides and Welch, “Forensic Biology: Serology and 

DNA,” p. 39.

281 . Isaacson et al., “Robust Detection of Individual Forensic 

Profiles in DNA Mixtures,” Forensic Science International: Genetics, 

Vol. 14, January 2015, pp. 31–37.

282. Eric Schwoebel, “Genomic Analysis and Human Identification,” briefing slides and discussions with author, Lexington, MA: MIT Lincoln Laboratory, January 9, 2013. Also, Melissa 
162

Gymrek et al., “Indentifying Personal Genomes by Surname Inference,” Science, January 18, 2013, available from www.sciencemag.

org/content/339/6117/321.full.

283. Douglas Shontz, DNA as Part of Identity Management for 

the Department of Defense, Santa Monica, CA: RAND, 2010, p. 9.

284. David Pendall, Global Operations and Biometrics: Next Generation Capabilities and Policy Implications, Carlisle, PA: U.S. Army 

War College, 2013, p. 4.

285. Andrew Pollack, “Building a Face, and a Case, on DNA,” 

The New York Times, February 23, 2015, available from www.

nytimes.com/2015/02/24/science/building-face-and-a-case-on-dna.

html?_r=0.

286. Pendall, p. 26.

287. Sandra I. Erwin, “As Defense, Intelligence Agencies 

Drown in Data, Technology Comes to the Rescue,” National 

Defense Magazine, November 2014, available from www.national

defensemagazine.org/archive/2014/November/Pages/AsDefenseIntellige

nceAgenciesDrowninData,TechnologyComestotheRescue.aspx.

288. Aparna Garg and Allan Ramsay, “Semantic Content 

Analysis of Video: Issues and Trends,” W. Lin et al., eds., Multimedia Analysis, Processing & Communications, Berlin, Germany: 

Springer-Verlag, 2011, pp. 443–457.

289. John, Markoff, “Researchers Announce Advance in 

Image-Recognition Software,” The New York Times, November, 

17, 2014, available from www.nytimes.com/2014/11/18/science/

researchers-announce-breakthrough-in-content-recognition-software.

html?_r=0.

290. Tom Simonite, “A Startup’s Neural Network Can Understand Video,” MIT Technology Review, February 3, 2015, available 

from www.technologyreview.com/news/534631/a-startups-neural-network-can-understand-video/.

291. For a discussion of this research, see Andrew Gallagher, 

Clint Mathialagan and Dhruv Batra, “VIP: Finding Important 
163

People in Images,” February 9, 2015, available from www.arxiv.

org/abs/1502.05678.

292. Tom Simonite, “Google’s Brain-Inspired Software Describes What It Sees in Complex Images,” MIT Technology Review, 

November 18, 2014, available from www.technologyreview.com/

news/532666/googles-brain-inspired-software-describes-what-it-seesin-complex-images.

293. George Seffers, “Tag Teaming Big Data,” Big Data ebook, 

Fairfax, VA: AFCEA International, 2014, p. 13.

294. Conor Dougherty, “Google Translate App Gets an Upgrade,” The New York Times, January 14, 2015, available from www.

bits.blogs.nytimes.com/2015/01/14/google-translate-app-gets-an-upgr

ade/?hp&action=click&pgtype=Homepage&module=second-columnregion&_r=0.

295 . Hardy, “Facebook Offers Artificial Intelligence Tech to 

Open Source Group.”

296. Davey Alba, “The Startup That Helps You Analyze Twitter Chatter in Real Time,” Wired Magazine, February 12, 2015, 

available from www.wired.com/2015/02/luminoso/.

297. Ben Shneiderman, Carsten Gorg, Chaomei Chen, Jim 

Thomas, John Stasko, and Pak Chung Wong, “Graph Analytics—

Lessons Learned and Challenges Ahead,” IEEE Computer Graphics 

and Applications, Vol. 33, No. 5, September/October 2011, pp. 18-

29, available from www.cs.umd.edu/~ben/papers/Wong2011Graph.

pdf.

298. William Campbell, Charlie Dagli, and Clifford Weinstein, 

“Social Network Analysis with Content and Graphs,” Lincoln Laboratory Journal, Vol. 20, No. 1, 2013, pp. 61-81, available from www.

ll.mit.edu/publications/journal/pdf/vol20_no1/20_1_5_Campbell.pdf.

299 . David Stillwella, Michal Kosinskia, and Thore Graepel, 

“Private Traits and Attributes are Predictable from Digital Records of Human Behavior,” Proceedings of the National Academy of Sciences, Early Edition, March 11, 2013, available from www.

pnas.org/cgi/doi/10.1073/pnas.1218772110.
164

300. For one such example, see Ana-Maria Popescu and 

Marco Pennacchiotti, “A Machine Learning Approach to Twitter 

User Classification,” Proceedings of the Fifth International AAAI 

Conference on Weblogs and Social Media, Association for the 

Advancement of Artificial Intelligence, 2011, available from www.

aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewFile/2886/3262.

301. These traits represent the so-called “big five,” or the fivefactor model of personality considered to be the most comprehensive, reliable, and useful set of personality concepts. For discussion of use of social media for personality analysis, see Daniele 

Quercia, David Stillwell, Jon Crowcroft, and Michal Kosinski, 

“Our Twitter Profiles, Our Selves: Predicting Personality with 

Twitter,” available from www.cl.cam.ac.uk/~dq209/publications/

quercia11twitter.pdf.

302. Renaud Lambiotte and Michal Kosinski, “Tracking 

the Digital Footprints of Personality,” Proceedings of the IEEE, 

Vol. 102, No. 12, December 2014, pp. 1934-1939, available from 

ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6939627&url=http

%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Ficp.jsp%3Farnumber

%3D6939627.

303. Ibid.

304. Siobhan Peters and Craig Denholm, “Channel: Protecting 

Vulnerable People from Being Drawn into Terrorism: A Guide 

for Local Partnerships,” London, UK: Office for Security and 

Counter-Terrorism, Home Office, October 2012, available from 

www.gov.uk/government/uploads/system/uploads/attachment_data/

file/118194/channel-guidance.pdf.

305. Mark Townsend, “How a Team of Social Media Experts 

Is Able to Keep Track of the UK Jihadis,” The Guardian, January 

17, 2015, available from www.theguardian.com/world/2015/jan/17/

social-media-british-jihadists-islamic-state-facebook-twitter.

306. Katrin Bennhold, “Fertile Ground for Militancy in Hometown of Jihadi John,” The New York Times, February 28, 2015, available from www.nytimes.com/2015/03/01/world/europe/fertile-groundfor-militancy-in-hometown-of-jihadi-john.html?_r=0.
165

307. Social Network Analysis with Content and Graphs, p. 73.

308. Jihun Hamm, Adam Champion, Guoxing Chen, Mikhail 

Belkin, and Dong Xuan, “Crowd-ML: A Privacy-Preserving 

Learning Framework for a Crowd of Smart Devices,” Columbus, 

OH: The Ohio State University, Department of Computer Science 

and Engineering, January 11, 2015, available from www.arxiv.org/

abs/1501.02484.

309. Social Network Analysis with Content and Graphs, p. 68.

310. Eric Schmitt, “U.S. Intensifies Effort to Blunt ISIS’ Message,” The New York Times, February 16, 2015, available from 

www.nytimes.com/2015/02/17/world/middleeast/us-intensifies-effortto-blunt-isis-message.html?_r=0.

311. Derek O’Callaghan, Derek Greene, Joe Carthy, Maura 

Conway, Nico Prucha, and Padraig Cunningham, “Online Social 

Media in the Syria Conflict: Encompassing the Extremes and the 

In-Betweens,” research paper sponsored by the Cybercrime Centers of Excellence Network and Science Foundation Ireland (SFI), 

August 13, 2014, available from www.arxiv.org./abs/1401.7535.

312. See discussion of the computational science and information science campaign plans in the Technical Implementation Plan: 

2015-2019, Adelphi, MD: U.S. Army Research Laboratory, January 2015, available from www.arl.army.mil/www/pages/172/docs/

ARL_Technical_Implementation_Plan.pdf.

313. Elizabeth Dwoskin, “Sleuthing Search Engine: Even Better Than Google?” The Wall Street Journal, February 12, 2015, available from www.wsj.com/articles/sleuthing-search-engine-even-betterthan-google-1423703464.

314. Rita Boland, “Novel Big Data Reveals Global Human 

Behavior,” Big Data ebook, Fairfax, VA: AFCEA International, 

2014, p. 13.

315. For background information, see the homepage for the 

Institute for the Study of Violent Groups, available from www.

isvg.org/organization.php.
166

316. Amelia Thomson-Deveaux, “Tracking 125,000 Incidents 

of Global Terrorism,” FiveThirtyEight, January 23, 2015, available 

from fivethirtyeight.com/features/the-paris-attacks-are-just-a-few-of125000-entries-in-the-global-terrorism-database.

317. U.S. Army Training and Doctrine Command (TRA- DOC) 

Pamphlet (TP) 525-3-1, The U.S. Army Operating Concept: Win in a 

Complex World, Fort Eustis, VA: TRADOC, 2014, p. 33.

318. Barrack Obama, National Security Strategy of the United 

States, Washington, DC: The White House, February 2015.

319. Rosa Brooks, “There’s No Such Thing as Peacetime,” 

Foreign Policy, March 13, 2015.

320. Lieutenant General Michael Barbero, Testimony before 

the House of Representatives Committee on Homeland Security 

Subcommittee on Cybersecurity, Infrastructure Protection and 

Security Technologies, Washington, DC, July 12, 2012.
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