NISTIR 7564 

Directions in 

Security Metrics Research 

Wayne Jansen 
Directions in 

Security Metrics Research

Wayne Jansen 

NISTIR 7564 

C O M P U T E R S E C U R I T Y

Computer Security Division 

Information Technology Laboratory 

National Institute of Standards and Technology 

Gaithersburg, MD 20899-8930

April 2009 

U.S. Department of Commerce 

Gary Locke, Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Deputy Director 

ii
Reports on Computer Systems Technology 

The Information Technology Laboratory (ITL) at the National Institute of Standards and 

Technology (NIST) promotes the U.S. economy and public welfare by providing technical 

leadership for the Nation’s measurement and standards infrastructure. ITL develops tests, test 

methods, reference data, proof of concept implementations, and technical analysis to advance the 

development and productive use of information technology. ITL’s responsibilities include the 

development of technical, physical, administrative, and management standards and guidelines for 

the cost-effective security and privacy of sensitive unclassified information in Federal computer 

systems. This Interagency Report discusses ITL’s research, guidance, and outreach efforts in 

computer security, and its collaborative activities with industry, government, and academic 

organizations. 

Certain commercial entities, equipment, or materials may be identified in this 

document in order to describe an experimental procedure or concept adequately. 

Such identification is not intended to imply recommendation or endorsement by the 

National Institute of Standards and Technology, nor is it intended to imply that the 

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Interagency Report 

26 pages (2009)

iii
Abstract 

More than 100 years ago, Lord Kelvin insightfully observed that measurement is vital to 

deep knowledge and understanding in physical science. During the last few decades, 

researchers have made various attempts to develop measures and systems of measurement 

for computer security with varying degrees of success. This paper provides an overview of 

the security metrics area and looks at possible avenues of research that could be pursued to 

advance the state of the art. 

Keywords: Security Metrics; Computer Security, Security Evaluation

Acknowledgements 

This report received the support of many individuals. The author is grateful to Tim Grance 

for suggesting the topic and encouraging its development. The author also wishes to thank 

colleagues who reviewed drafts of this document and contributed to its technical content, 

as well as the individuals who reviewed the public-release draft of this document and 

provided comments during the review period. Improvements to the content would not 

have been possible without their feedback and valuable suggestions. In particular, Paul 

Black, Lee Badger, and Russell Cameron Thomas offered useful and thought-provoking 

insight on the subject matter. However, the author takes full responsibility for any 

remaining errors or omissions in the report. 

iv
Table of Contents 

1. Introduction......................................................................................................1

2. Background......................................................................................................3

3. Aspects of Security Measurement ...................................................................5

3.1 Correctness and Effectiveness .................................................................5

3.2 Leading Versus Lagging Indicators...........................................................6

3.3 Organizational Security Objectives...........................................................7

3.4 Qualitative and Quantitative Properties ....................................................8

3.5 Measurements of the Large Versus the Small..........................................9

4. Possible Research Areas...............................................................................10

4.1 Formal Models of Security Measurement and Metrics............................10

4.2 Historical Data Collection and Analysis ..................................................11

4.3 Artificial Intelligence Assessment Techniques ........................................12

4.4 Practicable Concrete Measurement Methods.........................................13

4.5 Intrinsically Measurable Components .....................................................14

5. Conclusion .....................................................................................................15

6. References ....................................................................................................16

v
1. Introduction 

“We thought we understood what we were writing. We also 

thought the community would understand what we had written 

– or at least what we intended to have written. That 

turned out not to be the case.” – Marvin Schaefer, 

If A1 is the answer, what was the question? [Sch04] 

Security metrics is an area of computer security that has been receiving a good deal of 

attention lately. It is not a new topic, but one which receives focused interest sporadically. 

Much of what has been written about security metrics is definitional, aimed at providing 

guidelines for defining a security metric and specifying criteria for which to strive. 

However, relatively little has been reported on actual metrics that have been proven useful 

in practice [Ber05, CIS08]. 

Information security metrics are seen as an important factor in making sound decisions 

about various aspects of security, ranging from the design of security architectures and 

controls to the effectiveness and efficiency of security operations. Security metrics strive 

to offer a quantitative and objective basis for security assurance. The main uses fall into 

several broad classes: 

 Strategic support – Assessments of security properties can be used to aid 

different kinds of decision making, such as program planning, resource 

allocation, and product and service selection. 

 Quality assurance – Security metrics can be used during the software 

development lifecycle to eliminate vulnerabilities, particularly during code 

production, by performing functions such as measuring adherence to secure 

coding standards, identifying likely vulnerabilities that may exist, and tracking 

and analyzing security flaws that are eventually discovered. 

 Tactical oversight – Monitoring and reporting of the security status or posture 

of an IT system can be carried out to determine compliance with security 

requirements (e.g., policy, procedures, and regulations), gauge the 

effectiveness of security controls and manage risk, provide a basis for trend 

analysis, and identify specific areas for improvement. 

Security metrics can be categorized various ways. One simple classification is to consider 

metrics that denote the maturity level of processes believed to contribute to the security of 

a system, versus those that denote the extent to which some security characteristic is 

present in a system [Jel00]. The former apply to security processes, procedures, and 

training used when designing, configuring, maintaining, and operating a system. The latter 

apply to the security posture of a system and the inherent level of risk involved. More 

elaborate security metric taxonomies also exist [Vau02, Sav07]. 
Other disciplines, such as the field of finance, have proven quantitative methods for 

determining risk along with decision-making frameworks based on established measures 

and metrics. Such standardized measurements and decision-aid capabilities are just 

emerging for information system security, however, and as in any discipline, require 

realistic assumptions and inputs to attain reliable results [Sal09]. Advancing the state of 

scientifically sound, security measures and metrics (i.e., a metrology for information 

system security) would greatly aid the design, implementation, and operation of secure 

information systems. 

2
2. Background 

The term “security metrics” is used often today, but with a range of meanings and 

interpretations. Several examples taken from recent publications are as follows: 

“At a high-level, metrics are quantifiable measurements of some aspect of a 

system or enterprise. For an entity (system, product, or other) for which security 

is a meaningful concept, there are some identifiable attributes that collectively 

characterize the security of that entity. Further, a security metric (or 

combination of security metrics) is a quantitative measure of how much of that 

attribute the entity possesses. A security metric can be built from lower-level 

physical measures.” [ISS08] 

“Metrics are tools designed to facilitate decision making and improve 

performance and accountability through collection, analysis, and reporting of 

relevant performance-related data. The purpose of measuring performance is to 

monitor the status of measured activities and facilitate improvement in those 

activities by applying corrective actions, based on observed measurements. ... 

While a case can be made for using different terms for more detailed and 

aggregated items, such as ‘metrics’ and ‘measures,’ this document uses these 

terms interchangeably.” [Swa03] 

“Measurements provide single-point-in-time views of specific, discrete factors, 

while metrics are derived by comparing to a predetermined baseline two or 

more measurements taken over time. Measurements are generated by counting; 

metrics are generated from analysis. In other words, measurements are 

objective raw data and metrics are either objective or subjective human 

interpretations of those data.” [Pay06] 

A metric generally implies a system of measurement based on quantifiable measures. A 

method of measurement used to determine the unit of a quantity can involve a measuring 

instrument, reference material, or measuring system. Measured values of properties should 

be linearly ordered and the method of measurement well defined, including details of how 

specific factors are to be measured or assessed and explanations of sources of uncertainty. 

For information system security, the measures are concerned with aspects of the system 

that contribute to its security. That is, security metrics involve the application of a method 

of measurement to one or more entities of a system that possess an assessable security 

property to obtain a measured value. From an organizational perspective, security 

measures and metrics should enable an organization to gauge how well it is meeting its 

security objectives. 

To be of value, the method of measurement employed should be reproducible, that is, 

capable of attaining the same result when performed independently by different competent 

evaluators. The result should also be repeatable, such that a second assessment by the 

same evaluators produces the same result. Relevance and timeliness are also implicit 
considerations, since it is of little benefit to have measures that are not meaningful or 

whose latency exceeds their usefulness. 

Note that the term “metrics” when used in the context of Information Technology (IT) is a 

bit of misnomer.1 It implies that traditional concepts in metrology, as used in physics and 

other areas of science and technology, apply equally to IT. That is not the case, however 

[Gra99, Vau02]. For example, the concepts of fundamental units, scales, and uncertainty 

prevalent in scientific metrics have not traditionally been applied to IT or have been 

applied less rigorously. It is also important to recognize that compared with more mature 

scientific fields, IT metrology is still emerging. Many physical properties began as a 

qualitative comparison (e.g., “warmer” and “colder”) before becoming a formally defined 

quantity (e.g., “temperature”), which holds promise for IT metrics in general, and IT 

system security metrics in particular. 

While some movement toward quantitative metrics for IT system security exists, in 

practice, qualitative measures that reflect reasoned estimates of security by an evaluator are 

the norm. That is, measures of information system security properties are often based on 

an evaluator’s expertise, intuition, and insight to induce an ordering, which is then 

quantified (e.g., 1=low, 2=medium, 3=high). Because of the subjectivity involved, some 

of the attributes sought in a good metric are not readily obtainable. For example, results in 

penetration testing or other methods of assessment that involve specialized skills are 

sometimes not repeatable, since they rely on the knowledge, talent, and experience of an 

individual evaluator, which can differ from other evaluators with respect to a property 

being measured. 

 

1

 For example, the Proceeding of the 2001 Workshop on Information Security System Scoring and Ranking 

pointed out the discrepancy as “Information System Security Attribute Quantification or Ordering 

(Commonly but improperly known as ‘Security Metrics’).” [Hen01]. 

4
3. Aspects of Security Measurement 

Many major efforts to measure or assess security have been attempted. They include the 

Trusted Computer System Evaluation Criteria (TCSEC) [DOD85], Information 

Technology Security Evaluation Criteria (ITSEC) [CEC91], Systems Security Engineering 

Capability Maturity Model (SSE-CMM) [ISS08], and Common Criteria [CCP06]. Each 

attempt has obtained only limited success. 

It is reasonable to infer from the experience to date that security measurement is a tough 

problem, not to be underestimated [Bel06]. Further evidence is that the topic, EnterpriseLevel Security Metrics, was included in the most recent Hard Problem List prepared by the 

INFOSEC Research Council, which identifies key research problems from the perspective 

of its members, the major sponsors of information security research within the U.S. 

Government [IRC05]. The Institute for Information Infrastructure Protection (I3P) 

reported also identified security metrics as one of its four research and development 

priorities for the next five to ten years [I3P09]. 

Insights into some critical aspects of security measurement gleaned from past efforts are 

discussed below. The intent is not to give a list of common pitfalls, although some are 

mentioned in the discussion. Instead, the objective is to highlight those factors that are 

believed to be pertinent to a research effort in security metrics. 

3.1 Correctness and Effectiveness 

Security of an IT system comprises two interdependent aspects: correctness and 

effectiveness. Correctness denotes assurance that the security-enforcing mechanisms have 

been rightly implemented (i.e., they do exactly what they are intended to do, such as 

performing some calculation). Effectiveness denotes assurance that the security-enforcing 

mechanisms of the system meet the stated security objectives (i.e., they do not do anything 

other than what is intended for them to do, while satisfying expectations for resiliency). 

Most programmers have produced a program that satisfies correctness criteria, but fails to 

meet effectiveness criteria, particularly under extraordinary unanticipated conditions. 

 

The evaluation of correctness gauges the ability of the security-enforcing mechanisms to 

carry out their tasks precisely to the specifications. Correctness can be assessed with 

respect to the development process and the development environment during the 

construction of the system and also in terms of its operation. Emphasis is typically on 

substantiating how well the system exhibits the behavior expected of it. 

The evaluation of effectiveness gauges the strength of the security-enforcing mechanisms 

to withstand attacks in carrying out their function. Effectiveness requires ascertaining how 

well the security-enforcing components tie together and work synergistically, the 

consequences of any known or discovered vulnerabilities, and the usability of the system. 

Emphasis is typically on substantiating whether the system can be induced to exhibit 

behavior that leads to or demonstrates a security vulnerability. 
In practice, security evaluations of correctness and effectiveness are largely done through 

reasoning rather than direct measurement of actual hardware and software components. 

Often, simplifying assumptions are made. For example, it may be postulated that the 

system communicates only with other systems operating under the same management 

control and security policy constraints; the need to trust and communicate with external 

systems under different management control would not be specifically addressed [SAI07]. 

The emphasis on abstractions and simplifying assumptions dissociates the assessment 

results from actual operational use [Lit93]. Even with this emphasis, the correctness and 

effectiveness of significantly large systems cannot be accurately determined for higher 

levels of assurance. Because of the high degree of human element involved, the timeliness 

and reproducibility of results also come into question. Conventions and practices, such as 

standardized procedures and criteria, can be applied to help normalize results among 

evaluators and expedite the process. Technical refresher classes and conformance training 

are other useful practices. Nevertheless, unless more of the human element can be 

eliminated or replaced by automated means, the current conditions can be expected to 

persist. 

3.2 Leading Versus Lagging Indicators 

Analogous to economic indicators, security metrics may be potentially leading, coincident, 

or lagging indicators of the actual security state of the system. The distinction is 

significant. A coincident indicator reflects security conditions happening concurrently, 

while leading and lagging indicators reflect security conditions that exist respectively 

before or after a shift in security. If a lagging indicator is treated as a leading or coincident 

indicator, the consequences due to misinterpretation and reaction can be serious. The 

longer the latency period is for a lagging indicator, the greater the likelihood for problems. 

That is, a lagging security metric with a short latency period or lag time is preferred over 

one with a long latency period, since any needed response to an observed change can take 

place earlier. It is important to recognize lagging indicators and, if they are used, to be 

prepared to handle the intrinsic delay and associated limitations. 

Simple counts, when used as a security measure, can be especially hard to classify and 

interpret. For example, does an increase in the number of viruses detected by antivirus 

software serve as a leading indicator, because the increased activity indicates an elevated 

threat level; serve as a lagging indicator, because the increased activity demonstrates a 

highly proficient leading-edge antivirus mechanism; or serve as a coincident indicator, 

because the increased activity acts as a notification that other security-enforcing 

mechanisms are failing? Similarly, decreased activity may be because the antivirus 

mechanism is losing its effectiveness, other security-enforcing mechanisms are 

increasingly successful, or the system is simply not being subjected to as many attacks. 

Many security metrics can be viewed as lagging indicators. The perspective on the initial 

security assessments of a system, whether they were done by a human evaluator (e.g., 

through penetration testing or external security audit), automated means (e.g., through 

vulnerability testing or security log processing), or some combination of the two, is likely 

to change eventually to reflect a lower security standing and higher associated risk. The 

main reason is that over time, understanding of a system and its related weaknesses and 

6
vulnerabilities deepens, especially in light of successful attacks on the system or other 

systems sharing similar characteristics that reveal previously unimagined or unexpected 

avenues of attack. That greater understanding is subsequently addressed in updated 

assessments (e.g., through additional tests). While some assessment programs have a 

process to refresh or maintain initial security assessments, a significant delay nevertheless 

occurs. Stated another way, no metric exists that can denote the state of security of a 

system in an absolute sense [Tor07a, Tor07b]. 

Patches and software updates that are intended to repair or improve the security-enforcing 

mechanisms can lessen the significance of a lagging indicator. Not only does a reset of the 

baseline system occur with each change, potentially increasing risk [Bre07, Kei08, Lem08, 

Mar08, Nag08], but an exact picture of the initial baseline becomes obscured and more 

complicated to track over time (e.g., [Sto08]), making revised assessments impractical. 

That is, the presumed security posture of the initial baseline is never updated in light of 

increased understanding, potentially giving a false impression of the actual state. Several 

recent examples of dramatic shifts in our understanding of vulnerabilities in commonly 

used technologies, such as the Flash Player-ActionScript virtual machine [Dow08], 

OpenSSL cryptography [Dou08a, Gar08], MD5 algorithm in Web certificates [Pou08], and 

DNS protocol implementations [Dou08b], and also the prevalence of zero-day 

vulnerabilities indicate that with hindsight, unrevised values of lagging indicators could be 

highly misleading. 

3.3 Organizational Security Objectives 

Organizations exist for different purposes, hold different assets, have different exposure to 

the public, face different threats, and have different tolerances to risk. Because of these 

and other differences, their security objectives can vary significantly. For example, a 

government organization that mainly handles data about individual citizens of the country 

(e.g., taxes or social insurance) has different objectives than a government organization 

that does not (e.g., commerce or education). Similarly, the security objectives of a 

government organization that prepares and disseminates information for public 

consumption are different from one that deals mainly with classified information for its 

own use. In addition, practical considerations apply—most organizations cannot afford 

financially to protect all computational resources and assets at the highest degree possible 

and must prioritize based on criticality and sensitivity. 

Security metrics are generally used to determine how well an organization is meeting its 

security objectives. Since the security objectives of organizations can vary widely, it is 

reasonable to expect that the metrics required to make such an assessment for one 

organization would also be very different from those used for another. In other words, 

security is risk and policy dependent from an organizational perspective; the same platform 

populated with data at the same level of sensitivity, but from two different organizations, 

could be deemed adequate for one and inadequate for the other. The implication is that 

establishing security metrics that could be used for meaningful system comparisons 

between organizations would be extremely difficult to achieve (i.e., if achieved, they 

would have little real significance for some organizations). 

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Although security objectives are unique and tied to the goals and purpose of an 

organization, similarities in high level security objectives do exist between organizations 

performing similar work and are sometimes captured as best practices. Some steps, such 

as standard security profiles of organizational security requirements and criteria, can be 

taken to standardize common sets of core requirements needed by comparable 

organizations and allow reuse of engineered solutions. However, at best they cover only a 

common subset of the entire picture and may focus mainly on technical metrics. 

3.4 Qualitative and Quantitative Properties 

Measurement of software properties in general has been difficult to accomplish.2 Many 

desired properties such as complexity, usability, and scalability are qualities that can be 

expressed in general terms, but difficult to define in objective terms that are useful in 

practice and provide a fully complete picture. For example, two well known software 

complexity measures, the McCabe cyclomatic complexity metric and Halstead's software 

science metric, determine program complexity directly from source code [Mar97]. Critics 

claim that the number of control paths McCabe used to determine the complexity of code 

is only one part of the total picture. Similarly, some consider the emphasis placed on 

lexical and textual measures by Halstead to be weak, because the structural or logic flow is 

ignored, and consider the assumptions used to derive complexity equations to be faulty; 

furthermore, the equations can be difficult to compute and practical use is limited to 

comparisons between versions of the same code. At best, certain properties of software 

that are assessed are able to capture only some facets of any desired quality. 

The distinction between quantitative and qualitative security metrics is easily obscured 

[Hen01]. Qualitative assignments can be used to represent quantitative measures of 

security properties (e.g., low means no vulnerabilities found; medium, between one and 

five found; and high, more than five found). More often, numeric values are used to 

represent rankings that are otherwise qualitative (e.g., 1, 2, and 3, versus low, medium, and 

high). While the numeric difference between ranked values may be significant for some 

metrics, it may not be for others, which is often the case with security metrics. For 

example, the numeric difference between security rankings assigned through the analysis 

and reasoning of evaluators would likely not have any particular significance, other than to 

impart an ordering. In other words, the common scales of measurement (i.e., nominal, 

ordinal, interval, and ratio) and associated principles of use apply. 

Quantitative valuations of several security properties may also be weighted and combined 

to derive a composite value (e.g., rating = .25 * rankingA + .75 * rankingB). Such 

compositions can, however, yield undesired results. For example, the Common 

Vulnerability Scoring System (CVSS) v1 formula produced a lack of diversity in scoring, 

such that many cases of vulnerabilities with different characteristics received the same 

scores, but were clearly at significantly different levels of severity, which eventuated the 

formula’s revision [Rei07a]. 

 

2

 From a computability theory perspective, the general problem is undecidable, requiring its scope to be 

narrowed to a decidable sub-problem for a useful algorithm to be developed. 

8
Some qualitative properties are intangible and not able to be captured via direct 

measurement. An attribute, such as beauty, scent, or flavor, can be highly subjective in 

nature, varying widely among individuals. For example, a perfume’s fragrance might 

smell pleasant to one individual, but ghastly to another. In cases where no quantity can be 

clearly identified, such as the taste of wine, either a panel of experts rates various qualities 

using a blind rating (e.g., [Qua98]), or some measurable characteristics are assessed that 

are believed to correlate well with the quality in question (e.g., [AWB08]). Software 

security assessments appear to share many of the traits of these types of assessments and, 

where applicable, could possibly benefit by adopting or adapting some of the techniques 

used. 

3.5 Measurements of the Large Versus the Small 

Security measurements have proven to be much more successful when the target of 

assessment is small and simple rather than large and complex. For example, a FIPS 140 

evaluation, which focuses exclusively on cryptographic modules, generally requires less 

cost and time than a Common Criteria evaluation of a product that incorporates such 

modules. This is not too surprising, since larger systems generally have greater complexity 

and functionality. As the number of components in a system increases, the number of 

possible interactions increases with the square of the number of components. Greater 

complexity and functionality typically relate inversely to security and require more 

scrutiny to evaluate. 

Physical analogies apply here. The strength of a single concrete block of uniform material 

is more readily determined than a wall or structure composed of these blocks. The latter 

involve binding and reinforcing materials as well as height, design, and other architectural 

considerations. In other words, the different materials involved and the way in which they 

are composed determine the overall strength. 

The composability problem in security is a long-standing problem—two systems, both of 

which are judged to be secure, can be connected together such that the resulting composite 

system is not secure. In theory, evaluated systems could be designed to be predictably 

composable such that the properties of the resulting composite system are easily 

determined. In practice, this has not occurred. Composability is somewhat like the 

philosopher’s stone, a legendary substance that could change cheap metals into gold. A 

solution for building meaningful, complex software systems from components may exist 

theoretically; however, progress to date in achieving this goal has been limited, suggesting 

that composability is an elusive problem that may be solvable only in limited situations. 

A technological breakthrough in composability would be a way to have security 

measurements of small systems directly contribute to the measurements of larger systems 

of which they are a part. In the absence of sound security metrics that can be used to 

assess the security of composed systems made from composable components with 

measured properties, the current high latency and expense in evaluating large systems can 

be expected to continue and limit the ability to perform cross-system comparisons in 

security. 

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4. Possible Research Areas 

The implication from the previous section is that the present state of security metrics 

largely involves qualitative lagging indicators that require subjective evaluation and may 

not necessarily coincide with an organization’s security objectives. While this is a bleak 

picture, it does not mean that efforts to secure systems are pointless or that existing 

security metrics are useless. In order to improve the state of the art of security metrics, it 

can be argued that research efforts need to be focused on areas that satisfy one or more of 

the following factors: 

 Determine good estimators of system security. 

 Reduce reliance on the human element in measurement and inherent 

subjectivity. 

 Offer a more systematic and speedy means to obtain meaningful 

measurements. 

 Provide understanding and insight into the composition of security 

mechanisms. 

Based on the past attempts, it is clear that reaching this objective will likely not be simple. 

Nevertheless, the opportunity exists to build on previous work and find ways around or 

through present limitations. The remainder of this section considers some possible 

research areas to explore as a starting point. Its intent is not to be prescriptive, but rather to 

suggest a range of plausible activities that may hold promise. 

4.1 Formal Models of Security Measurement and Metrics 

Security measurement and metrics efforts that are conceived at a high level of abstraction 

and formalism are often difficult to interpret and apply in practice. Existing formalisms 

also pose difficulties to reconcile with actual operational environments where software 

patches, version updates, and configuration setting changes take place regularly. The 

absence of formal security models and other formalisms needed to improve the relevance 

of security metrics to deployed systems have hampered progress. Having formal models 

that depict security properties of operational IT systems and incorporate relevant objects of 

significance to system security measurement would be a useful contribution. The research 

goal is to establish formal models with a level of detail sufficient to enable realistic 

predictions of operational system behavior and portray security measurements accurately. 

An example of the type of work expected is the effort on attack surface metrics done at 

Carnegie Mellon University [Man05, Man07]. A formal model is defined from an intuitive 

notion of a system’s attack surface (i.e., the ways in which the system can be entered and 

successfully attacked). The formal model is characterized in terms of certain system 

resources—those methods (e.g., application programming interfaces), channels (e.g., 

sockets), and data items (e.g., input strings) that an attacker can use to cause damage to the 

system. A surface measurement model can then be applied to compare attack surface 

measurements along each of the three dimensions and used to determine whether one 
system is more secure than another. The measurements entail making estimates of the 

damage potential and effort required, which respectively are the level of harm the attacker 

can cause to the system when using the resource in an attack, and the amount of work 

needed by the attacker to obtain the necessary access rights to be able to use the resource in 

an attack. 

Model checking, a method for formally verifying properties of systems, is another area 

where a formal technique may have potential for use in security metrics. Properties of 

interest are expressed as logic formulas. A representation of the system (e.g., a formal or 

program language description) is traversed and checked to determine whether a property 

holds, using efficient symbolic algorithms. Specifying security properties of interest to be 

automatically checked could automate certain types of evaluation. For example, model 

checking has been used to identify an important class of security vulnerabilities in the Red 

Hat Linux 9 distribution. Vulnerabilities were expressed as temporal safety properties that 

described the conditions under which the vulnerabilities were absent from the software 

programs that made up the distribution [Sch05]. Programs that violated a property were 

not verifiable and flagged for review. Model checking has also been used to verify 

security properties of access control policies [Gue04, Rei07b]. 

Research into formal models could also benefit the design of decision support systems that 

manage security infrastructure risks by strongly embracing security metrics in determining 

security investments. Developing decision support models that incorporate technical and 

organizational aspects of a system and also quantify the utility of a security investment 

based on established principles would be the focus. The high level of detail and 

complexity of such models would likely depend strongly on the availability of essential 

empirical data. For instance, the QuERIES methodology and underlying attack-versusprotect economic model, which offers a means to determine investment levels and 

strategies for protecting intellectual property in complex systems, requires such data as 

asset valuations, the costs of protection and theft of assets, and theft probabilities [Car08]. 

4.2 Historical Data Collection and Analysis 

Predictive estimates of the security of software components and applications under 

consideration should be able to be drawn from historical data collected about the 

characteristics of other similar types of software (e.g., code quality and complexity) and 

the vulnerabilities they experienced (e.g., number and severity). For example, models that 

define software reliability in terms of the number of faults in a body of code have been 

applied to the OpenBSD operating system to estimate characteristics about the number of 

faults remaining in the system and when those faults may cause failures [Ozm06]. 

Empirical evidence also exists that features correlate with vulnerabilities and that 

components containing past vulnerabilities can be used to predict with reasonable accuracy 

vulnerable components (i.e., those having undiscovered vulnerabilities) based on their 

features [Neu07]. At the very least, insight into security measurements would likely be 

gained by applying analytical techniques to such historical collections to identify trends 

and correlations (e.g., via statistical methods), to discover unexpected relationships (e.g., 

via data mining), and to reveal other predictive interactions that may exist (e.g., via visual 

analytics). 

11
The research goal is to identify characteristics of software components and applications in 

the collection that can be extracted and used to predict the security condition of other 

software. Available open source software repositories can serve as a starting point for the 

data collection, but will require additional effort to incorporate vulnerability information 

and identify the points at which known vulnerabilities first appeared in the code set. With 

a large enough data collection, reliable estimators of the overall effectiveness of software 

might be derived that could be used in security assessments of software. 

A historical data collection has other potential benefits and use. It can serve as a basis for 

confirming the validity of independently proposed security measurements and methods of 

measurement, identifying whether associated measures are leading, lagging, or coincident 

indicators, and establishing estimates of latency and uncertainty for useful indicators. It 

can also serve as a means to investigate new methods of detecting and discovering both 

expected and unexpected relationships for use as estimators, and to develop new and 

improved mathematical and computational methodologies to improve the visual analytics 

of the data collection. A selected subset of the historical data collection could also be used 

as reference materials for training or rating the proficiency of security evaluators. 

4.3 Artificial Intelligence Assessment Techniques 

The field of Artificial Intelligence (AI) involves the design and implementation of systems 

that exhibit capabilities of the human mind, such as reasoning, knowledge, perception, 

planning, learning, and communication. AI encompasses a number of sub-disciplines 

including machine learning, constraint satisfaction, search, agents and multi-agent systems, 

reasoning, and natural language engineering and processing. While the use of AI has met 

with both successes and defeats, its application in aspects of security metrics might prove 

beneficial, particularly as a means for reducing subjectivity and human involvement in 

performing security assessments. 

The research goal is to identify areas of security evaluations that could be performed using 

AI or AI-assisted techniques and demonstrate their use. Dealing with uncertainty and 

inconsistency has been a part of AI from its origins. More recently, AI systems are 

beginning to emerge that can independently formulate, refine, and test hypotheses from 

observed data to uncover fundamental properties [Fig09]. Technologies for managing 

uncertainty and inconsistency have already been used in areas such as the ranking 

algorithms used in web search engines and are being applied to the broader area of 

homeland security [Che05]. The expectation is that AI technologies can play a similarly 

important role in the context of security assessments. 

For example, fuzzy logic is a superset of conventional logic that has been extended to 

handle the uncertainty in data. Fuzzy logic is useful in situations where it is difficult to 

make a precise statement about system behavior and has been applied successfully to the 

area of risk management [Don07, Mcg07, Sha03]. In these applications, qualitative risk 

descriptors, such as High, Medium, and Low, are able to be assigned to a range of values 

and calibrated as continuous quantitative input. In one case, fuzzy logic was used to assess 

the relative risk associated with computer network assets by ranking vulnerabilities with 

12
regard to the potential risk exposures of assets and networks [Don07]. In another case, 

using fuzzy logic provided risk analysts more information than qualitative approaches for 

ranking risks, to help them more effectively manage operational risks [Sha03]. 

4.4 Practicable Concrete Measurement Methods 

The current practice of security assessment, best illustrated by lower level evaluations 

under the Common Criteria, emphasizes the soundness of the evaluation evidence of the 

design and the process used in developing a product over the soundness of the product 

implementation. The rationale is that without a correct and effective design and 

development process, a correct and effective implementation is not possible. While this is 

true, the emphasis on design and process evidence versus actual product software largely 

overshadows practical security concerns involving the implementation and deployment of 

operational systems. The research goal is to devise methods of measurement that address 

vulnerabilities occurring in implementation and deployment and complementing existing 

security assessment practices that emphasize design and development process evidence. 

The intent is to be able to detect vulnerabilities that otherwise would escape detection. 

Code analysis has been a traditional way to examine code automatically and identify 

software problems that stem from violations in standard coding practices. Code analysis 

tools that are capable of identifying different types of potentially exploitable vulnerabilities 

have also been developed for security [Cha06, Mic06]. They provide a good example of 

one type of concrete measurement method possible, which is reproducible, repeatable, 

relevant, and timely. Although such security analysis tools provide highly useful results, 

they detect vulnerabilities incompletely and generate significant numbers of false positives, 

allowing room for improvement (e.g., [Sch05, Kir06]). 

Relatively little is known about software behavior or misbehavior after deployment 

[Bow02, Lib04]. Traditional means of feedback such as error or vulnerability reporting 

often are imprecise, contain inaccuracies, and involve delay, making it difficult to form a 

complete picture representative of the situation. Cooperative Bug Isolation (CBI) is an 

innovative technique that uses software instrumentation and sparse random sampling to 

gather information from the actual operation of deployed software [Lib04]. Algorithms 

have been developed for finding and fixing software errors based on statistical analysis of 

sparse feedback data. While developed for debugging purposes, the potential seemingly 

exists to extend the framework as a method of measurement for security through selective 

predicate instrumentation. 

Various forms of black box security testing offer another example of a possible type of 

concrete measurement method. For example, fuzzing is a type of fault injection technique 

that involves sending various types of pseudorandom data to available interfaces to 

discover unknown flaws present in programs and systems [Jur06]. Fuzzing techniques 

have been shown to be an effective means for detecting security vulnerabilities. 

Robustness testing is a closely related technique, which involves determining how well a 

software system or component functions when subjected to invalid inputs or other 

conditions that induce stress on the implementation. Incorrect behaviors exhibited under 

13
stressful conditions can often lead to potential security exploits such as denial of service 

[Kak01, Ron02]. 

4.5 Intrinsically Measurable Components 

Development of computing components that are inherently attuned to measurement would 

be a significant improvement in the state of the art of security metrics. The idea is to build 

components that clearly exhibit properties that matter to security. The research goal lies 

mainly on issues of mechanism and component design that facilitate or promote security 

measurement. For example, preparing strength of mechanism arguments in conjunction 

with the design and development of a security-enforcing component might be one method. 

Lower and upper bounds could be established, similar to the way performance bounds are 

calculated for sorting, matching, and other essential algorithms used in computing. 

Cryptographic mechanisms are an area where research results exist and bounds on the 

effort required to breach components could be determined, under specific assumptions. 

Extending this type of analysis to trust mechanisms is a more challenging problem, but one 

with significant benefits, if achieved. Components that rely on certain surety mechanisms, 

such as authentication modules designed for passwords or biometric modules for 

fingerprints, lend themselves to certain types of strength analysis (e.g., password space size 

[Lee08]), and results already exist that could be applied. Finally, techniques for strength 

analysis might also be drawn from physical security, such as metrics used by industry to 

evaluate safe and vault security and identify weaknesses leading to failure [Bla04]. 

Another possible area involves the application of evaluation criteria to system design. One 

possibility is to determine whether a methodology can be formulated for stipulating how to 

compose individually evaluated components of systems, such that the security of the 

overall system is ensured. The Trusted Network Interpretation (TNI) served this purpose 

for the TCSEC, but it was tied to Department of Defense security policies and a small 

standardized subset of security profiles. Attempting to devise a similar type of 

methodology for the Common Criteria would be more difficult because of the vast range of 

protection profiles that are able to be specified and which already exist. Nevertheless, it 

may be possible to narrow that scope to a definable composable subset through the 

systematic application of rules, principles, and logic. 

14
5. Conclusion 

The security metrics area poses hard and multi-faceted problems for researchers. Quick 

resolution is not expected and the likelihood is that not all aspects of the problem are 

resolvable. Furthermore, only some of those aspects that are resolvable may be able to be 

done satisfactorily, meeting expectations of repeatability, reproducibility, relevance, 

timeliness, and cost. Several factors impede progress in security metrics: 

 The lack of good estimators of system security. 

 The entrenched reliance on subjective, human, qualitative input. 

 The protracted and delusive means commonly used to obtain measurements. 

 The dearth of understanding and insight into the composition of security 

mechanisms. 

This paper proposes several lines of research that address these factors and could help to 

progress the state of the art in security metrics. The following research areas were 

identified: 

 Formal Models of Security Measurement and Metrics 

 Historical Data Collection and Analysis 

 Artificial Intelligence Assessment Techniques 

 Practicable Concrete Measurement Methods 

 Intrinsically Measurable Components. 

Each area in itself is quite extensive and requires a commitment to sustain the long-term 

research and development needed to be successful. 
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