Patent Publication Number: US-2022224723-A1

Title: Ai-driven defensive cybersecurity strategy analysis and recommendation system

Description:
CROSS CE TO RELATED APPLICATIONS 
     Priority is claimed in the application data sheet to the following patents or patent applications, the entire mitten description, including figures, of each of which is expressly incorporated herein by reference in its entirety: 
     Ser. No. 16/779,801 
     Ser. No. 16/777,270 
     Ser. No. 16/720,383 
     Ser. No. 15/823,363 
     Ser. No. 15/725,274 
     Ser. No. 15/655,113 
     Ser. No. 15/616,427 
     Ser. No. 14/925,974 
     Ser. No. 16/779,801 
     Ser. No. 16/777,270 
     Ser. No. 16/720,383 
     Ser. No. 15/823,363 
     Ser. No. 15/725,274 
     Ser. No. 15/655,113 
     Ser. No. 15/237,625 
     Ser. No. 15/206,195 
     Ser. No. 15/186,453 
     Ser. No. 15/166,158 
     Ser. No. 15/141,752 
     Ser. No. 15/091,563 
     Ser. No. 14/986,536 
     Ser. No. 14/925,974 
    
    
     BACKGROUND OF INVENTION 
     Field of the Invention 
     The disclosure relates to the field of computer systems, and more particularly to the field of cybersecurity analysis and improvements. 
     Discussion of the State of the Art 
     Modern networked systems are highly complex and vulnerable to attack from a myriad of constantly-evolving attack strategies using sophisticated algorithms that target both known and unknown vulnerabilities in hardware and software. The complexity of defending such systems from attack increases exponentially with the size of the systems, not linearly, because each component of the organization&#39;s network connects to multiple other components resulting in a combinatorial explosion. Current methodologies for improving cybersecurity defenses are largely reactive, depending on discovery of new attack strategies and providing patches in response, which is slow and leaves networked systems vulnerable to new attack strategies until they are patched. 
     What is needed is a system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a business cost/benefit analysis tailored to the operations of each enterprise environment and informed by the role and criticality of the data and services provided. 
     SUMMARY OF THE INVENTION 
     Accordingly, the inventor has developed a system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators. 
     According to a preferred embodiment, a system for automated cybersecurity defensive strategy analysis and recommendations is disclosed, comprising: an attack implementation engine comprising a first plurality of programming instructions stored in a memory of, and operating on a processor of, a computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to: receive test initiation instructions; implement a cyberattack on a network under test; and gather system information about the operation of the network under test during the cyberattack, the system information comprising information about the sequence of events and response of affected devices during the cyberattack; a simulator comprising a second plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to: receive the system information; use the system information to initiate an iterative simulation of a cyberattack strategy sequence, each iteration comprising a simulated attack on a model of the network under test and a simulated defense against the simulated attack, each simulated attack and each simulated defense being generated by a first machine learning algorithm; obtain a simulation result comprising the cyberattack strategy sequence and a probability of success of the attack and the defense in each iteration; a recommendation engine comprising a third plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to: receive the simulation result; receive one or more cost factors; receive one or more benefit factors; compare the simulation result against the cost factors and the benefit factors; determine a cybersecurity improvement recommendation for the network under test based on the comparison; a malware detection system comprising a fourth plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the fourth plurality of programming instructions, When operating on the processor, cause the computing device to: capture packets from a connected network; analyze captured packets from a connected network; wherein the packets are sent to or from another device on the same network; classify the captured packets as benign or malicious; wherein the captured packets may be classified individually or in some combination, based on their contents and the metadata of the packets; and a security logic engine comprising a fifth plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the fifth plurality of programming instructions, when operating on the processor, cause the computing device to: correlate maliciously classified packets from a malware detection system, with known malicious behavior; analyze the risk of detected malware; and report the malware to administrators. 
     According to another preferred embodiment, a method for automate cybersecurity defensive strategy analysis and recommendations is disclosed, comprising the steps of: receiving test initiation instructions; implementing a cyberattack on a network under test; gathering system information about the operation of the network under test during the cyberattack, the system information comprising information about the sequence of events and response of affected devices during the cyberattack; using the system information to initiate an iterative simulation of a cyberattack strategy sequence, each iteration comprising simulated attack on a model of the network under test and a simulated defense against the simulated attack, each simulated a Hack and each simulated defense being generated by a first machine learning algorithm; obtaining a simulation result comprising the cyberattack strategy sequence and a probability of success of the attack and the defense in each iteration; receiving one or more cost factors; receiving one or more benefit factors; comparing the simulation result against the cost factors and the benefit factors; determining a cybersecurity improvement recommendation for the network under test based on the comparison; capturing packets from a connected network, using a malware detection system; analyzing captured packets from a connected network, using a malware detection system; wherein the packets are sent to or from another device on the same network, using a malware detection system; classifying the captured packets as benign or malicious, using a malware detection system; wherein the captured packets may be classified individually or in some combination, based on their contents and the metadata of the packets, using a malware detection system; correlating maliciously classified packets from a malware detection system, with known malicious behavior, using a security logic engine; analyzing the risk of detected malware, using a security logic engine; and reporting the malware to administrators, using a security logic engine. 
     According to an aspect of an embodiment, the system information further comprises system logs of one or more of the affected devices. 
     According to an aspect of an embodiment, the machine learning algorithm is an evolutionary algorithm. 
     According to an aspect of an embodiment, the simulation is an online simulation and the evolutionary algorithm is a continual online evolutionary planning algorithm. 
     According to an aspect of an embodiment, a second machine learning algorithm is used, wherein each simulated attack is generated by the first machine learning algorithm and each simulated defense is generated by the second machine learning algorithm, such that the algorithms compete against each oilier in the simulation. 
     According to an aspect of an embodiment, the cybersecurity improvement recommendation is implemented on the network under test. 
     According to an aspect of an embodiment, process is run iteratively, with each iteration resulting in a new cybersecurity improvement recommendation, which is implemented on the network under lest, prior to the next iteration. 
     BRIEF DESCRIPTION OF THE DRAWING FIGURES 
     The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary, and are not to be considered as limiting of the scope of the invention or the claims herein in any way. 
       FIG. 1  is a block diagram fan exemplary system architecture for an advanced cyber decision platform for external network reconnaissance and cybersecurity rating. 
       FIG. 2A  is a block diagram showing general steps for performing passive network reconnaissance. 
       FIG. 2B  is a process diagram shoring a general flow of a process for performing active reconnaissance using DNS leak information collection. 
       FIG. 2C  is a process diagram showing a general flow of a process for performing active reconnaissance using web application and technology reconnaissance. 
       FIG. 2D  is a process diagram showing a general flow of a process for producing a cybersecurity rating using reconnaissance data. 
       FIG. 3A  is a process diagram showing data sources for a business operating system for use in mitigating cyberattacks. 
       FIG. 3B  is a process diagram showing business operating system functions in use to mitigate cyberattacks. 
       FIG. 4  is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties. 
       FIG. 5  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
       FIG. 6  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
       FIG. 7  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
       FIG. 8  is a flow diagram of an exemplary method for cybersecurity behavioral analytics, according to one aspect. 
       FIG. 9  is a flow diagram of an exemplary method for measuring the effects of cybersecurity attacks, according to one aspect. 
       FIG. 10  is a flow diagram of an exemplary method for continuous cybersecurity monitoring and exploration, according to one aspect. 
       FIG. 11  is a flow diagram of an exemplary method for mapping a cyber-physical system graph, according to one aspect. 
       FIG. 12  is a flow diagram of an exemplary method for continuous network resilience rating, according to one aspect. 
       FIG. 13  is a flow diagram of an exemplary method for cybersecurity privilege oversight, according to one aspect. 
       FIG. 14  is a flow diagram of an exemplary method for cybersecurity risk management, according to one aspect. 
       FIG. 15  is a flow diagram of an exemplary method for mitigating compromised credential threats, according to one aspect. 
       FIG. 16  is a flow diagram of an exemplary method for dynamic network and rogue device discovery, according to one aspect. 
       FIG. 17  is a flow diagram of an exemplary method for Kerberos “golden ticket” attack detection, according to one aspect. 
       FIG. 18  is a flow diagram of an exemplary method for risk-based vulnerability and patch management, according to one aspect. 
       FIG. 19  is block diagram showing an exemplary system architecture for a system for cybersecurity profiling and rating. 
       FIG. 20  is a relational diagram showing the relationships between exemplary 3 rd  party search tools, search tasks that can be generated using such tools, and the types of information that may be gathered with those tasks. 
       FIG. 21  is a relational diagram showing the exemplary types and classifications of information that may be used in constructing a cyber-physical graph of an organization&#39;s infrastructure and operations. 
       FIG. 22  is a directed graph diagram showing an exemplary cyber-physical graph and its possible use in analyzing cybersecurity threats. 
       FIG. 23  is a block diagram showing exemplary operation of a data to rule mapper. 
       FIG. 24  is block diagram showing an exemplary architecture diagram for a scoring engine. 
       FIG. 25  (PRIOR ART) is a block diagram showing an exemplary process control system integrated with an information technology system. 
       FIG. 26  is a block diagram showing an exemplary architecture for a system for parametric analysis of integrated operational technology systems and information technology systems. 
       FIG. 27  is a directed graph diagram showing an example of the use of a cyber-physical graph to model a simple salinity adjustment process control system. 
       FIG. 28  is a method diagram showing how parametric analysis of integrated operational technology and information technology systems may be employed to detect cybersecurity threats. 
       FIG. 29  is a diagram of an exemplary architecture of a system for the capture and storage of time series data from sensors with heterogeneous reporting profiles according to an embodiment of the invention. 
       FIG. 30  is a block diagram showing an exemplary system architecture for an automated cybersecurity defensive strategy analysis and recommendation system. 
       FIG. 31  is a block diagram showing an exemplary system architecture for a machine learning simulator for an automated cybersecurity defensive strategy analysis and recommendation system. 
       FIG. 32  is a block diagram showing exemplary inputs to a recommendation engine for an automated cybersecurity defensive strategy analysis and recommendation system. 
       FIG. 33  is a block diagram illustrating an exemplary hardware architecture of computing device. 
       FIG. 34  is a block diagram illus ting an exemplary logical architecture for a client device. 
       FIG. 35  is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services. 
       FIG. 36  is another block diagram illustrating an exemplary hardware architecture of a computing device. 
       FIG. 37  is a system diagram of a network-connected endpoint device operating malware detection agent software, according to an aspect. 
       FIG. 38  is a system diagram of a malware detection system, according to an aspect. 
       FIG. 39  is a system diagram of a security logic engine, according to an aspect. 
    
    
     DETAILED DESCRIFIION 
     The inventor has conceived, and reduced to practice, a system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators. 
     Modern networked systems are highly complex and vulnerable to attack from a myriad of constantly-evolving attack strategies using sophisticated automation and dedicated experts that target both known and unknown vulnerabilities in hardware and software. The complexity of defending such systems horn attack increases exponentially with the size of the systems, not linearly, because each component of the organization&#39;s network connects to multiple other components resulting in a combinatorial explosion. Current methodologies for improving cybersecurity defenses are largely reactive, depending on discovery of new attack strategies and providing patches in response, which is slow and leaves networked systems vulnerable to new attack strategies until they are patched. 
     A major drawback of reactive cybersecurity defensive strategies is that they are unable to predict and counter evolving attack strategies. Even where attempts are made by humans to predict the next evolution in a strategy and anticipate a defense, such attempts are limited in that they are unable to account for the tremendous complexity and interactions of modern networked systems. However, machine learning algorithms, and particularly evolutionary algorithms such as genetic algorithms, can be used to simulate the impact of evolving attack and defense strategies on models of highly complex systems and provide simulation results indicating probabilities of evolution of attack and defense strategies in certain directions and the impact to a network&#39;s cybersecurity defenses if attack and defense strategies to evolve in a certain direction. In this way, likely attack strategies can be predicted and defense strategies can be developed or anticipated before an attack occurs, even if the attack has not yet been developed in the real world. Further, a cost/benefit analysis of the developed or anticipated defensive strategies can be performed to provide recommendations as to which of the developed or anticipated defensive strategies to implement on the real-world networked system being modeled in the simulation. 
     Cybersecurity defensive recommendations implemented on the real-world networked system can be reflected in the system model, and a new round of machine learning simulations can be run to identify new attack strategies that may be used on the changed system. This iterative machine learning process narrows the possible avenues of attack on a networked system, first identifying attack strategies with a high likelihood of success and large impacts, and in each subsequent iteration identifying attack strategies with lower and lower likelihoods of success. The recommendation engine provides the cost-benefit analysis to determine at what point it no longer makes economic sense to implement defensive strategies in certain directions. 
     A major benefit of this system is its automation. The system can be made to be entirely automated, running iteration after iteration and implementing recommended changes to the networked system through the use of changes in software configurations, access controls, etc., even including isolating certain hardware from the network through software controls. Implementation of recommendations to physically alter hardware components (e.g., physical removal of a server or physical disconnection of cables would require manual input and a user interface for administrative control and operation is included. 
     The better and more detailed a model represents a real-world system, the better the predictive capability of the model (i.e., greater model accuracy reduces the level of uncertainty, which leads to better predictions). However, the more complex the model, the harder it is to run the model. More computing resources are required to account for an exponentially-increasing set of interactions. Sonic problems are essentially infeasible to model with current computing capabilities due to this combinatorial explosion, and therefore un-computable at the level of real-world detail. 
     The current algorithms for analyzing complex systems are limited and insufficient for this purpose. In order to make the problem tractable for computing, they either lack sufficient detail in the models about the systems being analyzed in order or limit the number of dimensions analyzed in the model, or both. One example of this limitation is modeling of rare-event simulations in complex systems that are caused by a confluence of factors. If the model is not sufficiently detailed, it will not capture low-level variables that impact the rare event, regardless of how complex the analysis is. Conversely, if the analysis is limited to selected dimensions, it will not capture complex interactions that combine to cause the rare event, even if the model is sufficiently detailed. 
     The solution is to allow for iterative, scalable parametric analysis, which allows for identification of critical components in the analysis and limiting of low-level analyses to key components while generalizing higher-level analyses for non-key components. This allows for creation of detailed and robust system models, yet still allows high-dimensionality analyses of the detailed models with finite computing resources. Increased granularity in models and calibration of models to real-world data may be used to obtain improved performance and risk management, by allowing for testing of system adjustments against More precise and robust models prior to implementation of the adjustments on real-world systems. Further, such improved models and analyses may be used to predict rare events that depend on a very specific confluence of factors for occurrence, or to detect unpredictable events such as infiltration of the operational technology system by malware (for example, a worm like Stuxnet). 
     One or more different aspects may be described in the present application. Further, for one or more of the aspects described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the aspects contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage in the one or more particular aspects or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the aspects nor a listing of features of one or more of the aspects that must be present in all arrangements. 
     Headings of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in any way. 
     Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more communication means or itermediaries, logical or physical. 
     A description of an aspect with several components in communication with each other does not imply that all such components are required. To the contrary a variety of optional components may be described to illustrate a wide variety of possible aspects and in order to more fully illustrate one or more aspects. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence. 
     When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article. 
     The functionality or the features of a device may be alternatively embodied one or more other devices that are not explicitly described as having such functionality or features. Thus, other aspects need not include the device itself. 
     Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing nodules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out, of order from that, shown or discussed, including substantially concurrently or in reverse order, depending on functionality involved, as would be understood by those having ordinary skill in the art. 
     Definitions 
     “Artificial intelligence” or “AI” as used herein means a computer system or component that has been programmed in such a way that it mimics some aspect or aspects of cognitive functions that humans associate with human intelligence, such as learning, problem solving, pattern recognition, and decision-making. Examples of current AI technologies include understanding human speech, competing successfully in strategic games such as chess and Go, autonomous operation of vehicles, complex simulations, and interpretation of complex data such as images and video. 
     “Machine learning” as used herein is an aspect of artificial intelligence in which the computer system or component can modify its behavior or understanding without being explicitly programmed to do so. There are three primary classifications of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms attempt to infer functions from labeled training datasets, which functions can then be applied to new datasets. Unsupervised leaning algorithms attempt to find previously unknown patterns in unlabeled datasets. Reinforcement learning algorithms attempt to find optimal actions to be taken by maximizing sonic reward. 
     As used herein, a “swimlane” is a communication channel between a time series sensor data reception and apportioning device and a data store meant to hold the apportioned to tune series sensor data. A swimlane is able to move a specific, finite amount of data between the two devices. For example, a single swimlane might reliably carry and have incorporated into the data store, the data equivalent of 5 seconds worth of data from 10 sensors in 5 seconds, this being its capacity. Attempts to place 5 seconds worth of data received from 6 sensors using one swimlane would result in data loss. 
     As used herein, a “metaswimlane” is an as-needed logical combination of transfer capacity of two or more real swimlanes that is transparent to the requesting process. Sensor studies where the amount of data received per unit time is expected to be highly heterogeneous over time may be initiated to use metaswimlanes. Using the example used above that a single real swimlane may transfer and incorporate the 5 seconds worth of data of 10 sensors without data loss, the sudden receipt of incoming sensor data from 13 sensors during a 5 second interval would cause the system to create a two swimlane metaswimlane to accommodate the standard  10  sensors of data in one real swimlane and the 3 sensor data overage in the second, transparently added real swimlane, however no changes to the data receipt logic would be needed as the data reception and apportionment device would add the additional real swimlane transparently. 
     As used herein,“graph” representation of information and relationships, where each primary unit of information makes up a “node” or “vertex” of the graph and the relationship between two nodes makes up an edge of the graph. Nodes can be further qualified by the connection of one or more descriptors or “properties” to that node. For example, given the node “James R,” name information for a person, qualifying properties might be “183 cm tall”, “DOB Aug. 13, 1965” and “speaks English”. Similar to the use of properties to further describe the information in a node, a relationship between two nodes that forms an edge can be qualified using a “label”. Thus, given a second node “Thomas G,” an edge between “James R” and “Thomas G” that indicates that the two people know each other n t be labeled “knows.” When graph theory notation (Graph-(Vertices, Edges)) is applied this situation, the set of nodes are used as one parameter of the ordered pair, V and the set of 2 element edge endpoints are used as the second parameter of the ordered pair, E. When the order of the edge endpoints within the pairs of E is not significant, for example, the edge James R, Thomas G is equivalent to Thomas G, James R, the graph is designated as “undirected.” Under circumstances when a relationship flows from one node to another in one direction, for example James R is “taller” than Thomas G, the order of the endpoints is significant. Graphs with such edges are designated as “directed.” In the distributed computational graph system, transformations within transformation pipeline are represented as directed graph with each transformation comprising a node and the output messages between transformations comprising edges. Distributed computational graph stipulates the potential use of non-linear transformation pipelines which are programmatically linearized. Such linearization can result in exponential growth of resource consumption. The most sensible approach to overcome possibility is to introduce new transformation pipelines just as they are needed, creating only those that are ready to compute. Such method results in transformation graphs which are highly variable in size and node, edge composition as the system processes data streams. Those familiar with the art will realize that transformation graph may assume many shapes and sizes with a vast topography of edge relationships. The examples given were chosen for illustrative purposes only and represent a small number of the simplest of possibilities. These examples should not be taken to define the possible graphs expected as part of operation of the invention 
     As used herein “transformation” is a function performed on zero or more streams of input data which results in a single stream of output which may or may not then be used as input for another transformation. Transformations may comprise any combination of machine, human or machine-human interactions Transformations need not change data that enters them, one example of this type of transformation would be a storage transformation which would receive input and then act as a queue for that data for subsequent transformations. As implied above, a specific transformation may generate output data in the absence of input data. A time stamp serves as a example. In the invention, transformations are placed into pipelines such that the output of one transformation may serve as an input for another. These pipelines can consist of two or more transformations with the number of transformations limited only by the resources of the system. Historically, transformation pipelines have been linear with each transformation in the pipeline receiving input from one antecedent and providing output to one subsequent with no branching or iteration. Other pipeline configurations are possible. The invention is designed to permit several of these configurations including, but not limited to: linear, afferent branch, efferent branch and cyclical. 
     A “database” or “data storage subsystem” (these terms may be considered substantially synonymous), as used herein, is a system adapted for the long-term storage, indexing; and retrieval of data, the retrieval typically being via some sort of querying interface or language. “Database” may be used to refer to relational database management systems known in the art, but should not be considered to be limited to such systems. Many alternative database or data storage system technologies have been, and indeed are being, introduced in the art, including but not limited to distributed non-relational data storage systems such as Hadoop, column-oriented databases, in-memory databases, and the like. While various aspects may preferentially employ one or another of the various data storage subsystems available in the art (or available in the future), the invention should not be construed to be so limited, as any data storage architecture may be used according to the aspects. Similarly, while in some cases one or more particular data storage needs are described as being satisfied by separate components (for example, an expanded private capital markets database and a configuration database), these descriptions refer to functional uses of data storage systems and do not refer to their physical architecture. For instance, any group of data storage systems of databases referred to herein may be included together in a single database management system operating on a single machine, or they may be included in a single database management system operating on a cluster of machines as is known in the art. Similarly, any single database (such as an expanded private capital markets database) may be implemented on a single machine, on a set of machines using clustering technology, on several machines connected by one or n ore messaging systems known in the art, or in a master/slave arrangement common in the art. These examples should make clear that no particular architectural approaches to database management is preferred according to the invention, and choice of data storage technology is at the discretion of each implementer, without departing from the scope of the invention as claimed. 
     A “data context”, as used herein, refers to a set of arguments identifying the location of data. This could be a Rabbit queue, a .csv file in cloud-based storage, or any other such location reference except a single event or record. Activities may pass either events or data contexts to each other for processing. The nature of a pipeline allows for direct information passing between activities, and data locations or files do not need to be predetermined at pipeline start. 
     “Information technology” or “IT” as used herein means the development, maintenance, and use of computer systems, software, and networks for the processing and distribution of data. Typically, but not exclusively, the term information technology is associated use of computer systems, software, and networks for the business operations of an organization, and not for control of physical systems. 
     “Operational technology” as used herein means use of computer systems, software, and networks to monitor and alter the state of a physical system. Operational technology is often referred to as process control technology or process control systems. Operational technology systems typically include supervisory control and data acquisition (SCADA) systems, distributed control systems (DCS), Remote Terminal Units (RTU) and programmable logic controllers (PLC), as well as dedicated networks and organization units. Examples of large scale operational technology are systems for controlling power stations, oil and gas refineries, or railways. Embedded Systems are also included in the sphere of operational technology, and the term can include small scale control systems such as for the engine control unit (ECU) of a modern car. 
     “Parametric analysis” is used herein to mean an experiment or test designed to discover the differential effects of a range of values of an independent variable. 
     A “pipeline”, as used herein and interchangeably referred to as a “data pipeline” or a “processing pipeline”, refers to a set of data streaming activities and batch activities. Streaming and batch activities can be connected indiscriminately within a pipeline. Events will flow through the streaming activity actors in a reactive way. At the junction of a streaming activity to batch activity, there will exist a StreamBatchProtocol data object. This object is responsible for determining when and if the batch process is run. One or more of three possibilities can be used for processing triggers: regular timing interval, every N events, or optionally an external trigger. The events are held in a queue or similar until processing. Each batch activity may contain a “source” data context (this may be a streaming context if the upstream activities are streaming), and a “destination”data context (which is passed to the next activity). Streaming activities may have an optional “destination” streaming data context (optional meaning: caching/persistence of events vs. ephemeral), though this should not be part of the initial implementation. 
     Conceptual Architecture 
       FIG. 1  is a block diagram of an advanced cyber decision platform for external network reconnaissance and cybersecurity rating. Client access to the system  105  for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, occurs through the system&#39;s distributed, extensible high bandwidth cloud interface  110  which uses a versatile, robust web application drivers interface fir both input and display of client-facing information via network  107  and operates a data store  112  such as, but not limited to MONGODB™, COUCHDDB™, CASSANDRA™ or REDIS™ according to various arrangements. Much of the business data analyzed by the system both from sources within the confines of the client business, and from cloud based sources, also enter the system through the cloud interface  110 , data being passed to the connector module  135  which may possess the API routines  135   a  needed to accept and convert the external data and then pass the normalized information to other analysis and transformation components of the system, the distributed computational graph module  155 , high volume web crawler module  115 , multidimensional time series database (MDTSDB)  120  and the graph stack service  145 . The distributed computational graph module  155  retrieves one or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a plurality of physical sensors, network service providers, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information. Within the distributed computational graph module  155 , data may be split into two identical streams in a specialized pre-programmed data pipeline  155   a,  wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis data is then transferred to the general transformer service module  160  for linear data transformation as part of analysis or the decomposable transformer service module  150  for branching or iterative transformations that are part of analysis. The distributed computational graph module  155  represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. The high volume web crawling module  115  uses multiple server hosted preprogrammed web spiders, which while autonomously configured are deployed within a web scraping framework  115   a  of which SCRAPY™ is an example, to identify and retrieve data of interest from web based sources that are not well tagged by conventional web crawling technology. The multiple dimension time series data store module  120  may receive streaming data from a large plurality of sensors that may be of several different types. The multiple dimension time series data store module may also store any time series data encountered by the system such as but not limited to enterprise network usage data, component and system logs, performance data, network service information captures such as, but not limited to news and financial feeds, and sales and service related customer data. The module is designed to accommodate irregular and high volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data. Inclusion of programming wrappers  120   a  for languages examples of which are, but not limited to C++, PERL, PYTHON, and ERLANG™ allows sophisticated programming logic to be added to the default function of the multidimensional time series database  120  without intimate knowledge of the core programming, greatly extending breadth of function. Data retrieved by the multidimensional time series database (MDTSDB)  120  and the high volume web crawling module  115  may be further analyzed and transformed into task optimized results by the distributed computational graph  155  and associated general transformer service  150  and decomposable transformer service  160  modules. Alternately, data from the multidimensional time series database and high volume web crawling modules may be sent, often with scripted cuing information determining important vertexes  145   a,  to the graph stack service module  145  which, employing standardized protocols for converting streams of information into graph representations of that data, for example, open graph internet technology although the invention is not reliant on any one standard. Through the steps, the graph stack service module  145  represents data in graphical form influenced by any pre-determined scripted modifications  145   a  and stores it in a graph-based data store  145   b  such as GIRAPH™ or a key value pair type data store REDIS™, or RIAK™, among others, all of which are suitable for storing graph-based information. 
     Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to the already available data in the automated planning service module  130  which also runs powerful information theory  130   a  based predictive statistics functions and machine learning algorithms to allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions. The using all available data, the automated planning service module  130  may propose business decisions most likely to result is the most favorable business outcome with a usably high level of certainty. Closely related to the automated planning service module in the use of system derived results in conjunction with possible externally supplied additional information in the assistance of end user business decision making, the action outcome simulation module  125  with its discrete event simulator programming module  125   a  coupled with the end user facing observation and state estimation service  140  which is highly scriptable  140   b  as circumstances require and has a game engine  140   a  to more realistically stage possible outcomes of business decisions under consideration, allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data. 
     When performing external reconnaissance via a network  107 , web crawler  115  may be used to perform a variety of port and service scanning operations on a plurality of hosts. This may be used to target individual network hosts (for example, to examine a specific server or client device) or to broadly scan any number of hosts (such as all hosts within particular domain, or any number of hosts up to the complete address space). Port scanning is primarily used for gathering information about hosts and services connected to a network, using probe messages sent to hosts that prompt a response from that host. Port scanning is generally centered around the transmission control protocol (TCP), and using dye information provided in a prompted response a port scan can provide information about network and application layers on the targeted host. 
     Port scan results can yield information on open, dosed, or undetermined ports on a target host. An open port indicated that an application or service is accepting connections on this port (such as ports used for receiving customer web traffic on a web server), and these ports generally disclose the greatest quantity of useful information about the host. A closed port indicates that no application or service is listening for connections on that port, and still provides information about the host such as revealing the operating system of the host, which may discovered by fingerprinting the TCP/IP stack in a response. Different operating systems exhibit identifiable behaviors when populating TCP fields, and collecting multiple responses and matching the fields against a database of known fingerprints makes it possible to determine the OS of the host even ports are open. An undetermined port is one that does not produce a requested response, generally because the port is being filtered by a firewall on the host or between the host and the network (for example, a corporate firewall behind which all internal servers operate). 
     Scanning may be defined by scope to limit the scan according to two dimensions, hosts and ports. A horizontal scan checks the same port on multiple hosts, often used by attackers to check for an open port on any available hosts to select a target for an attack that exploits a vulnerability using that port. This type of scan is also useful for security audits, to ensure that vulnerabilities are not exposed on any of the target hosts. A vertical scan defines multiple ports to examine on a single host, for example a “vanilla scan” which targets every port of a single host, or a “strobe scan” that targets a small subset of ports on the host. This type of scan is usually performed for vulnerability detection on single systems, and due to the single-host nature is impractical for large network scans. A block scan combines elements of both horizontal and vertical scanning, to scan multiple ports on multiple hosts. This type of scan is useful for a variety of service discovery and data collection tasks, as it allows a broad scan of many hosts (up to the entire Internet, using the complete IPA address space) for a number of desired ports in a single sweep. 
     Large port scans involve quantitative research, and as such may be treated as experimental scientific measurement and are subject to measurement and quality standards to ensure the usefulness of results. To avoid observational errors during measurement, results must be precise (describing a degree of relative proximity between individual measured values), accurate (describing relative proximity of measured values to a reference value), preserve any metadata that accompanies the measured data, avoid misinterpretation of data due to faulty measurement execution, and must be well-calibrated to efficiently expose and address issues of inaccuracy or misinterpretation. In addition to these basic requirements, large volumes of data may lead to unexpected behavior of analysis tools, and extracting a subset to perform initial analysis may help to provide an initial overview before working with the complete data set. Analysis should also be reproducible, as with all experimental science, and should incorporate publicly-available data to add value to the comprehensibility of the research as well as contributing to a “common framework” that may be used to confirm results. 
     When performing a port scan, web crawler  115  may employ a variety of software suitable for the task, such as Nmap, ZMap, or masscan. Nmap is suitable for large scans as well as scanning individual hosts, and excels in offering a variety of diverse scanning techniques. ZMap is a newer application and unlike Nmap (which is more general-purpose), ZMap is designed specifically with Internet-wide scans as the intent. As a result, ZMap is far less customizable and relies on horizontal port scans for functionality, achieving fast scan times using techniques of probe randomization (randomizing the order in which probes are sent to hosts, minimizing network saturation) and asynchronous design (utilizing stateless operation to send and receive packets in separate processing threads). Masscan uses the same asynchronous operation model of ZMap, as well as probe randomization. In masscan however, a certain degree of statistical randomness is sacrificed to improve computation time for large scans (such as when scanning the entire IPv4 address space), using the BlackRock algorithm. This is a modified implementation of symmetric encryption algorithm DES, with fewer rounds and modulo operations in place of binary ones to allow for arbitrary ranges and achieve faster computation time for large data sets. 
     Received scan responses may be collected and processed through a plurality of data pipelines  155   a  to analyze the collected information, MDTSDB  120  and graph stack  145  may be used to produce a hybrid graph time series database using the analyzed data, forming a graph of Internet-accessible organization resources and their evolving state information over time. Customer-specific profiling and scanning information may be linked to CPG graphs (as described below in detail, referring to  FIG. 11 ) for a particular customer, but this information may be further linked to the base-level graph of internet-accessible resources and information. Depending on customer authorizations and legal or regulatory restrictions and authorizations, techniques used may involve both passive, semi-passive and active scanning and reconnaissance. 
       FIG. 2A  is a block diagram showing general steps  200  for performing passive network reconnaissance. It should be appreciated that the steps illustrated and described may be performed in any order, and that steps may be added or omitted as needed for any particular reconnaissance operation. In a step  201 , network address ranges and domains or sub-domains associated with a plurality of targets may be identified, for example to collect information for defining the scope of further scanning operations. In another step  202 , external sites may be identified to understand relationships between targets and other third-party content providers, such as trust relationships or authoritative domain name service (DNS) resolution records. In another step  203 , individual people or groups may be identified using names, email addresses, phone numbers, or other identifying information that may be useful for a variety of social engineering activities. In another step  204 , technologies used may be identified, such as types or versions of hardware or software used by an organization, and this may include collecting and extracting information from job descriptions (for example) to identify technologies in use by an organization (for example, a job description for an administrator familiar with specific database software indicates that said software is in use within the organization). In another step  205 , content of interest may be identified, for example including web and email portals, log files, backup or archive files, and other forms of sensitive information that may be contained within HTML comments or client-side scripts, as may be useful for vulnerability discovery and penetration testing activities. In another step  206 , publicly-available information may be used to identify vulnerabilities that may be exploited with further active penetration testing. 
       FIG. 2B  is a process diagram showing a general flow of a process  210  for performing active reconnaissance using DNS leak information collection. In an initial step  211 , publicly-available DNS leak disclosure information may be collected to maintain current information regarding known leaks and vulnerabilities. In a next step  212 , third-level domain (TLDR) information may be collected and used to report domain risk factors, such as domains that do not resolve properly (due to malformed DNS records, for example). In a next step  213 , a DNS trust map may be created using a hybrid graph/time series data structure, using a graph stack service  145  and MDTSDB  120 . This trust map may be produced as the output of an extraction process performed by a DCG  155  through a plurality of data pipelines  155   a , analyzing collected data and mapping data points to produce hybrid structured output representing each data point over time. In a final step  214 , the trust map may then be analyzed to identify anomalies, for example using community detection algorithms that may discover when new references are being created, and this may be used to identify vulnerabilities that may arise as a byproduct of the referential nature of a DNS hierarchy. In this manner, DCG pipeline processing and time series data graphing may be used to identify vulnerabilities that would otherwise be obscured within a large dataset. 
       FIG. 2C  is a process diagram showing a general flow of a process  220  for performing active reconnaissance using web application and technology reconnaissance. In an initial step  221 , a plurality of manual HTTP requests may be transmitted to a host, for example to determine if a web server is announcing itself, or to obtain an application version number from an HTTP response message. In a next step  222 , a robots.txt; used to identify and communicate with web crawlers and other automated “bots”, may be searched for to identify portions of an application or site that robots are requested to ignore. In a next step  223 , the host application layer may be fingerprinted, for example using file extensions and response message fields to identify characteristic patterns or markers that may be used to identify host or application details. In a next step  224 , publicly-exposed/admin pages may be checked, to determine if any administrative portals are exposed and therefore potentially-vulnerable, as well as to potentially determine administration policies or capabilities based on exposed information. In a finial step  225 , an application may be profiled according to a particular toolset in use, such as WORDPRESS™ (for example) or other specific tools or plugins. 
       FIG. 2D  is a process diagram showing a general flow of a process  230  for producing a cybersecurity rating using reconnaissance data. In an initial step  231 , external reconnaissance may be performed using DNS and IP information as described above (referring to  FIG. 2B ), collecting information from DNS records, leak announcements, and publicly-available records to produce a DNS trust map from collected information and the DCG-driven analysis thereof. In a next step  232 , web and application recoil may be performed (as described in  FIG. 2C ), collecting information on applications, sites, and publicly-available records. In a next step  233 , collected information over time may be analyzed for software version numbers, revealing the patching frequency of target hosts and their respective applications and services. Using a hybrid time series graph, timestamps may be associated with ongoing changes to reveal these updates over time. In a next step  234 , a plurality of additional endpoints may be scanned, such as (for example, including but not limited to) internet-of-things (IoT) devices that may be scanned and fingerprinted, end-user devices such as personal smartphones, tablets, or computers, or social network endpoints such as scraping content from user social media pages or feeds. User devices may be fingerprinted and analyzed similar to organization hosts, and social media content may be retrieved such as collecting sentiment from services like TWITTER™ LINKEDIN™, or analyzing job description listings and other publicly-available information. In a next step  235 , open-source intelligence feeds may be checked, such as company IP address blacklists, search domains, or information leaks (for example, posted to public records such as PASTEBIN™). In a final step  236 , collected information from all sources may be scored according to a weighted system, producing an overall cybersecurity rating score based on the information collected and tin analysis of that information to reveal additional insights, relationships, and vulnerabilities. 
     For example, in an exemplary scoring system similar to a credit rating, information from initial Internet recon operations may be assigned a score up to 400 points, along with up to 200 additional points for web/application recon results, 100 points for patch frequency, and 50 points each for additional endpoints and open-source intel results. This yields a weighted score incorporating all available information from all scanned sources, allowing a meaningful and readily-appreciable representation of an organization&#39;s overall cybersecurity strength. Additionally, as scanning may be performed repeatedly and results collected into a time series hybrid data structure, this cybersecurity rating may evolve over time to continuously reflect the current state of the organization, reflecting any recent changes, newly-discovered or announced vulnerabilities, software or hardware updates, newly-added or removed devices or services, and any other changes that may occur. 
       FIGS. 3A and 3B  are process diagrams showing a general flow of business operating system functions in rise to mitigate cyberattacks. Input network data which may include network flow patterns  321 , the origin and destination of each piece of measurable network traffic  322 , system logs from servers and workstations on the network  323 , endpoint data  329 , any security event log data from servers or available security information and event (SIEM) systems  324 , external threat intelligence feeds  324 , identity or assessment context  325 , external network health or cybersecurity feeds  326 , Kerberos domain controller or ACTIVE DIRECTORY™ server logs or instrumentation  327  and business unit performance related data  328 , among many other possible data types for which the invention was designed to analyze and integrate, may pass into  315  the business operating system  310  for analysis as part of its cyber security function. These multiple types of data from a plurality of sources may be transformed for analysis  311 ,  312  using at least one of the specialized cybersecurity, risk assessment or common functions of the business operating system in the role of cybersecurity system, such as, but not limited to network and system user privilege oversight  331 , network and system user behavior analytics  332 , attacker and defender action timeline  333 , SIEM integration and analysis  334 , dynamic benchmarking  335 , and incident identification and resolution performance analytics  336  among other possible cybersecurity functions; value at risk (VAR) modeling and simulation  341 , anticipatory vs. reactive cost estimations of different types of data breaches to establish priorities  342 , work factor analysis  343  and cyber event discovery rate  344  as part of the system&#39;s risk analytics capabilities; and the ability to format and deliver customized reports and dashboards  351 , perform generalized, ad hoc data analytics on demand  352 , continuously monitor, process and explore incoming data, for subtle changes or diffuse informational threads  353  and generate cyber-physical systems graphing  354  as part of the business operating system&#39;s common capabilities. Output  317  can be used to configure network gateway security appliances  361 , to assist in preventing network intrusion through predictive change to infrastructure recommendations  362 , to alert an enterprise of ongoing cyberattack early in the attack cycle, possibly thwarting it but at least mitigating the damage  362 , to record compliance to standardized guidelines or STA requirements  363 , to continuously probe existing network infrastructure and issue alerts to any changes which may make a breach more likely  364 , suggest solutions to any domain controller ticketing weaknesses detected  365 , detect, presence of malware  366 , and perform one time or continuous vulnerability scanning depending on client directives  367 , and thwart or mitigate damage from cyber attacks  368 . These examples are, of course, only a subset of the possible uses of the system, they are exemplary in nature and do not reflect any boundaries in the capabilities of the invention. 
       FIG. 4  is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties  400 . As previously disclosed  200 ,  351 , one of the strengths of the advanced cyber-decision platform is the ability to finely customize reports and dashboards to specific audiences, concurrently is appropriate. This customization is possible clue to the devotion of a portion of the business operating system&#39;s programming specifically to outcome presentation by modules which include the observation and state estimation service  140  with its game engine  140   a  and script interpreter  140   b.  In the setting of cybersecurity, issuance of specialized alerts, updates and reports may significantly assist in getting the correct mitigating actions done in the most, timely fashion while keeping all participants informed at predesignated appropriate granularity. Upon the detection of a cyberattack by the system  401  all available information about the ongoing attack and existing cybersecurity knowledge are analyzed, including through predictive simulation in near real time  402  to develop both the most accurate appraisal of current events and actionable recommendations concerning where the attack may progress and how it may be mitigated. The information generated in totality is often snore than any one group needs to perform their mitigation tasks. At this point, during a cyberattack, providing a single expansive and all inclusive alert, dashboard image, or report may make identification and action upon the crucial information by each participant more difficult, therefore the cybersecurity focused arrangement may create multiple targeted information streams each concurrently designed to produce most rapid and efficacious action throughout the enterprise during the attack and issue follow-up reports with and recommendations or information that may lead to long term changes afterward  403 . Examples of groups that may receive specialized information streams include but may not be limited to front line responders during the attack  404 , incident forensics support both during and after the attack  405 , chief information security officer  406  and chief risk officer  407  the information sent to the latter two focused to appraise overall damage and to implement both mitigating strategy and preventive changes after the attack. Front line responders may use the cyber-decision platform&#39;s analyzed, transformed and correlated information specifically sent to them  404  to probe the extent of the attack, isolate such things as: the predictive attacker&#39;s entry point onto the enterprise&#39;s network, the systems involved or the predictive ultimate targets of the attack and may use the simulation capabilities of the system to investigate alternate methods of successfully ending the attack and repelling the attackers in the most efficient manner, although many other queries known to those skilled in the art are also answerable by the invention. Simulations run may also include the predictive effects of any attack mitigating actions on normal and critical operation of the enterprise&#39;s IT systems and corporate users. Similarly, a chief information security officer may use the cyber-decision platform to predictively analyze  406  what corporate information has already been compromised, predictively simulate the ultimate information targets of the attack that may or may not have been compromised and the total impact of the attack what can be done now and in the near future to safeguard that information. Further, during retrospective forensic inspection of the attack, the forensic responder may use the cyber-decision platform  405  to clearly and completely map the extent of network infrastructure through predictive simulation and large volume data analysis. The forensic analyst may also use the platform&#39;s capabilities to perform a time series and infrastructural spatial analysis of the attack&#39;s progression with methods used to infiltrate the enterprise&#39;s subnets and servers. Again, the chief risk officer would perform analyses of what information  407  was stolen and predictive simulations on What the theft means to the enterprise as time progresses. Additionally, the system&#39;s predictive capabilities may be employed to assist in creation of a plan for changes of the IT infrastructural that should be made that are optimal for remediation of cybersecurity risk under possibly limited enterprise budgetary constraints in place at the company so as to maximize financial outcome. 
       FIG. 5  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph  500 , according to one aspect. According to the aspect, a DCG  500  may comprise a pipeline orchestrator  501  that may be used to perform a variety of data transformation functions on data within a processing pipeline, and may be used with a messaging system  510  that enables communication with any number of various services and protocols, relaying messages and translating them as needed into protocol-specific API system calls for interoperability with external systems (rather than requiring a particular protocol or service to be integrated into a DCG  500 ). 
     Pipeline orchestrator  501  may spawn a plurality of child pipeline clusters  502   a - b,  which may be used as dedicated workers for streamlining parallel processing. In sortie arrangements, an entire data processing pipeline may be passed to a child cluster  502   a  for handling, rather than individual processing tasks, enabling each child cluster  502   a - b  to handle an entire data pipeline in a dedicated fashion to maintain isolated processing of different pipelines using different cluster nodes  502   a - b.  Pipeline orchestrator  501  may provide a software API for starting, stopping, submitting, or saving pipelines. When a pipeline is started, pipeline orchestrator  501  may send the pipeline information to an available worker node  502   a - b,  for example using AKKA™ clustering. For each pipeline initialized by pipeline orchestrator  501 , a reporting object with status information may be maintained. Streaming activities may report the last time an event was processed, and the number of events processed. Batch activities may report status messages as they occur. Pipeline orchestrator  501  may perform batch caching using, for example, an IGFS™ caching filesystem. This allows activities  512   a - b  within a pipeline  502   a - b  to pass data contexts to one another, with any necessary parameter configurations. 
     A pipeline manager  511   a - b  may be spawned for every new running pipeline, and may be used to send activity, status, lifecycle, and event count information to the pipeline orchestrator  501 . Within a particular pipeline, a plurality of activity actors  512   a - d  may be created by a pipeline manager  511   a - b  to handle individual tasks, and provide output to data services  522   a - d.  Data models used in a given pipeline may be determined by the specific pipeline and activities, as directed by a pipeline manager  511   a - b.  Each pipeline manager  511   a - b  controls and directs the operation of any activity actors  512   a - d  spawned by it. A pipeline process may need to coordinate streaming data between tasks. For this, a pipeline manager  511   a - b  may spawn service connectors to dynamically create TCP connections between activity instances  512   a - d.  Data contexts may be maintained for each individual activity  512   a - d,  and may be cached for provision to other activities  512   a - d  as needed. A data context defines how an activity accesses information, and an activity  512   a - d  may process data or simply forward it to a next step. Forwarding data between pipeline steps may route data through a streaming context or batch context. 
     A client service cluster  530  may operate a plurality of service actors  521   a - d  to serve the requests of activity actors  512   a - d,  ideally maintaining enough service actors  521   a - d  to support each activity per the service type. These may also be arranged within service clusters  520   a - d,  in a manner similar to the logical organization of activity actors  512   a - d  within clusters  502   a - b  in a data pipeline. A logging service  530  may be used to log and sample DCG requests and messages during operation while notification service  540  may be used to receive alerts and other notifications during operation (for example to alert on errors, which may then be diagnosed by reviewing records from logging service  530 ), and by being connected externally to messaging system  510 , to logging and notification services can be added, removed, or modified during operation without impacting DCG  500 . A plurality of DCC protocols  550   a - b  may be used to provide structured messaging between a DCG  500  and messaging system  510 , or to enable messaging system  510  to distribute DCG messages across service clusters  520   a - d  as shown. A service protocol  560  may be used to define service interactions so that a DCC  500  may be modified without invading service implementations. In this manner it can be appreciated that the overall structure of a system using an actor-driven DCG  500  operates in a modular fashion, enabling modification and substitution of various components without impacting other operations or requiring additional reconfiguration. 
       FIG. 6  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph  500 , according to one aspect. According to the aspect, a variant messaging arrangement may utilize messaging system  510  as a messaging broker using a streaming protocol  610 , transmitting and receiving messages immediately using messaging system  510  as a message broker to bridge communication between service actors  521   a - b  as needed. Alternately, individual services  522   a - b  may communicate directly in a batch context  620 , using a data context service  630  as a broker to batch-process and relay messages between services  522   a - b.    
       FIG. 7  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph  500 , according to one aspect. According o the aspect, a variant messaging arrangement may utilize a service connector  710  as a central message broker between a plurality of service actors  521   a - b,  bridging messages in a streaming context  610  while a data context, service  630  continues to provide direct peer-to-peer messaging between&#39;dual services  522   a - b  in a batch context  620 . 
     It should be appreciated that various combinations and arrangements of the system variants described above. (referring to  FIGS. 1-7 ) may be possible, for example using one particular messaging arrangement for one data pipeline directed by a pipeline manager  511   a - b,  while another pipeline may utilize a different messaging arrangement or may not utilize messaging at all). In this manner, a single DCG  500  and pipeline orchestrator  501  may operate individual pipelines in the manner that is most suited to their particular needs, with dynamic arrangements being made possible through design modularity as described above in  FIG. 5 . 
       FIG. 19  is block diagram showing an exemplary system architecture  1900  for a system for cybersecurity profiling and rating. The system in this example contains a cyber-physical graph  1902  which is used to represent a complete picture of an organization&#39;s infrastructure and operations including, importantly, the organization&#39;s computer network infrastructure. The system further contains a distributed computational graph  1911 , which contains representations of complex processing pipelines and is used to control workflows through the system such as determining which 3 rd  party search tools  1915  to use, assigning search tasks, and analyzing the cyber-physical graph  1902  and comparing results of the analysis against reconnaissance data received from the reconnaissance engine  1906  and stored in the reconnaissance data storage  1905 . In some embodiments, the determination of which 3 rd  party search tools  1915  to use and assignment of search tasks may be implemented by a reconnaissance engine  1906 . The cyber-physical graph  1902  plus the analyses of data directed by the distributed computational graph on the reconnaissance data received from the reconnaissance engine  1906  are combined to represent the cyber-security profile of the client organization whose network  1907  is being evaluated. A queuing system  1912  is used to organize and schedule the search tasks requested by the reconnaissance engine  1906 . A data to rule mapper  1904  is used to retrieve laws, policies, and other rules from an authority database  1903  and compare reconnaissance data received from the reconnaissance engine  1906  and stored in the reconnaissance data storage  1905  against the rules in order to determine whether and to what extent the data received indicates a violation of the rules. Machine learning models  1901  may be used to identify patterns and trends in any aspect of the system, but in this case are being used to identify patterns and trends in the data which would help the data to rule mapper  1904  determine whether and to what extent certain data indicate a violation of certain rules. A scoring engine  1910  receives the data analyses performed by the distributed computational graph  1911 , the output of the data to rule mapper  1904 , plus event and loss data  1914  and contextual data  1909  which defines a context in which the other data are to be scored and/or rated. A public-facing proxy network  1908  is established outside of a firewall  1917  around the client network  1907  both to control access to the client network from the Internet  1913 , and to provide the ability to change the outward presentation of the client network  1907  to the Internet  1913 , which may affect the data obtained by the reconnaissance engine  1906 . In some embodiments, certain components of the system may operate outside the client network  1907  and may access the client network through a secure, encrypted virtual private network (VPN) 1916 , as in a cloud-based or platform-as-a-service implementation, but in other embodiments some or all of these components may be installed and operated from within the client network  1907 . 
     As a brief overview of operation, information obtained about the client network  1907  and the client organization&#39;s operations, which is used to construct a cyber-physical graph  1902  representing the relationships between devices, users, resources, and processes in the organization, and contextualizing cybersecurity information with physical and logical relationships that represent the flow of data and access to data within the organization including, in particular, network security protocols and procedures. The distributed computational graph  1911  containing workflows and analysis processes, selects one or more analyses to be performed on the cyber-physical graph  1902 . Sonic analyses may be performed on the information contained in the cyber-physical graph, and some analyses may be performed on or against the cyber-physical graph using information obtained from the Internet  1913  from reconnaissance engine  1906 . The workflows contained in the distributed computational graph  1911  select one or more search tools to obtain information about the organization from the Internet  1915 , and may comprise one or more third party search tools  1915  available on the Internet. As data are collected, they are fed into a reconnaissance data storage  1905 , from which they may be retrieved and further analyzed. Comparisons are made between the data obtained from the reconnaissance engine  1906 , the cyber-physical graph  1902 , the data to rule mapper, from which comparisons a cybersecurity profile of the organization is developed. The cybersecurity profile is sent to the scoring engine  1910  along with event and loss data  1914  and context data  1909  for the scoring engine  1910  to develop a score and/or rating for the organization that takes into consideration both the cybersecurity profile, context, and other information. 
       FIG. 24  is block diagram showing an exemplary architecture  2400  for a scoring engine. Data fed into the scoring engine comprise the cybersecurity profile  1918  and reconnaissance data  1905  developed at earlier stages of system operation. Based on these data, a frequency and severity of attack is estimated  2408 . For each risk type, curve lining  2402  may be performed on the data points to assign a “best fit” function along the range of data points, which captures trends in the data and allows for predictions of how similar data will behave in the future. Aggregations of operational variables  2403  may be applied to identify maxima, minima, counts, sums, and standard deviations of the data. Risk identification and quantification is then performed  2413 , and a business impact analysis is performed  2412  based on a totality of the predicted risks, their severity, business dependencies reflected in the cyber-physical graph, and prior event and loss data  2410 , among other variables. From this analysis of business impact  2412 , a network resilience rating is assigned  2405 , representing a weighted and adjusted total of relative exposure the organization has to various types of risks, each of which may be assigned a sub-rating. The network resilience rating  2405  may be a single score for all factors, a combination of scores, or a score for a particular risk or area of concern. The network resilience rating  2411  may then be adjusted or filtered depending on the context in which it is to be used  2409 . For example, context data received  2408  may indicate that the scores are to be used for compliance with internal theft policies, but the factors associated with the network resilience rating indicate that the highest risks are associated with cyber-attacks from external systems, which may cause the adjustment for goal/purpose  2409  to filter out the factors of the network resilience rating associated with risks from external cyber-attacks or reduce their contribution to a functional score. Finally, a functional cybersecurity score  2411  is assigned which takes into account the adjusted factors of the network resilience score and the context in which the functional score is to be applied. The process may be iterative, in that the network resilience rating  2405  from previous analyses may be fed back into the start of the process at estimation of frequency and severity of attacks  2401 . 
       FIG. 25  (PRIOR ART) is a block diagram showing an exemplary process control system integrated with an information technology system. In this simplified diagram, the process control system  2520  is controlled by a supervisory control and data acquisition (SCADA) unit, which is sometimes also referred to as a human machine interface (HMI). The SCADA/HMI unit displays information to a control system operator about the operation of the overall system and the state or status of various sub-systems and devices. Sub-systems and devices are each controlled by a programmable logic controller (PLC) or remote terminal unit (RTU)  2522   a - n,  which are dedicated computing devices programmed to control specific physical systems and devices such as valves, pumps, heaters, conveyor belts, etc. The PLC/RTUs  2522   a - n  receive data from sensors  2523   a - n  and either take action through their own programming or direction from the SCADA/HMI  2521  to send control signals to actuators  2524   a - n  which change the operation or state of the physical system or device (not shown). Process control systems  2520  often communicate with or are integrated with an IT infrastructure system  2510 , typically through one or more servers  2511 . In this simplified diagram, the server  2511  acts as the central hub which manages data traffic throughout the IT infrastructure system  2510 . The server  2511  routes information to and from to the SCADA/HMI system  2521 , one or more routers  2512  which route information to a plurality of workstations  2513   a - n  and other devices (not shown), storage  2514 , and a domain controller  2515  which controls access to the IT infrastructure from other networks  2516  such as the Internet, for example allowing remote access  2517  to the IT infrastructure from authorized systems and entities, but preventing access from other systems and entities. 
       FIG. 26  is a block diagram showing an exemplary architecture for a system for parametric analysis of integrated operational technology systems and information technology systems. In the embodiments described herein, one or more directed graphs are used to create system models  2610  to model both the operational technology (OT) and information technology (IT) systems and the interactions between them. A cyber-physical graph is used to model the entities and entity relationships of the IT system  2611  and a distributed computational graph is used to model the complex workflows and processes within the IT system  2613  as modeled by the cyber-physical graph of the IT system  2611 . A cyber-physical graph is used to model the entities and entity relationships of the IT system  2611  and a distributed computational graph is used to model the complex workflows and processes within the system  2613  as modeled by the cyber-physical graph of the IT system  2611 . Likewise, a cyber-physical graph is used to model the entities and entity relationships of the OT system  2612  and a distributed computational graph is used to model the complex workflows and processes within the OT system  2614  as modeled by the cyber-physical graph of the OT system  2612 . This methodology of using directed graphs to models the systems allows for a very fine level of granularity in the model and incorporation of broader range of variables than traditional modeling. While separate directed graphs are show in this example for each system and its workflows, it is possible to incorporate all of this information into a single graph or break the information into a series of smaller graphs. The interface between the cyber-physical graphs of OT and FP system models  2611 ,  2613  may be a separate cyber-physical graph or may be implied by inputs/outputs in each of the separate cyber-physical graphs of OT and IT system models  2611 ,  2613 . 
     A model analyzer  2620  is used to analyze scenarios run on the models and calibrate them to the real-world OT and IT systems that are being modeled. An in-situ data manager  2621  receives, organizes, and stores data obtained from the real-world operation of the OT and IT systems  2640  that are being modeled by the system models  2610 . These in-situ operational data may comprise any data generated by, or obtainable from, the OT and IT systems  2640 , including but not limited to device telemetry data, system and device log files, connection and access activity, network events, deployed software versions, user activity information, sensor data, process control status information, etc., and may be stored in a time series data store. 
     A simulator/comparator  2622  runs simulations on the system models  2610 , and compares the simulations to the in-situ operating data to calibrate the system models  2610  to the real-world systems  2640  being modeled. The simulator/comparator  2622  may be programmed to search for parameter values that maximize agreement between simulation output under varying conditions (whether actual or artificial) and in-situ operating data front the real-world OT/IT systems  2640 . Results of the simulations may be passed through machine learning algorithms (not shown) to identify trends or patterns in the data. The simulator/comparator  2622  may use the output of an iterative parameter calculator  2623  to search for parameter values that maximize agreement between simulation output under varying conditions (whether actual or artificial) and in-situ operating data front the real-world OT/IT systems  2640 . Results of the simulations may be passed through machine learning algorithims (not shown) to identify trends or patterns in the data. 
     An iterative parameter calculator  2623  can be used to iterate individual parameters or groups of parameters over a range of conditions to determine their impact on the individual system models  2610  or the system represented by the system models  2610  as a whole. In conjunction with the simulator/comparator  2622  to link observed phenomena (e.g., in-situ data as one example) and expectations from the system models  2610 , along with the IT/OT control system state, to help isolate whether observed effects are likely to be linked to operational changes, errors in OT systems including, for example, errors from malware such as Olympic Games/Stuxnet), or physical or process problems (e.g. a pipeline leak for an oil or gas transportation network). For example, the iterative parameter calculator  2623  may be used to isolate uncertainty in outcomes based on different contributing factors. This can include sampling from a given parameter to determine the uncertainty in the overall model output, prioritizing exploration of factors (internal or exogenous) contributing to deviations in the expected mean or median performance of a system, quantification of the overall variability in model response or the reliability of a given set of operational criteria being met or maintained over a finite time horizon (which may be used as a reasonable proxy for reliability estimation), or to simply determine the range or intervals of possible outcomes, particularly when frequency of occurrence may be not be capable of estimation so severity of an occurrence, must take priority in the analysis. The results obtained from the iterative parameter calculator  2623  may also enable statistical validation metrics or estimates associated with performance changes possible from RL type approaches. An artificial load generator  2624  may be used by the iterative parameter calculator to iterate parameters of the system while under a simulated load (e.g., bandwidth and data usage for IT systems, physical process conditions such as temperature, flow rates, etc., for OT systems, and the like). 
     A key feature of the system for parametric analysis is the scaling optimizer  2630  which, with the help of the simulator/comparator  2622  and iterative parameter calculator  2623 , identifies key components of the system models  2610  and scales the analyses to make them tractable from the standpoint of finite computing resources while maintaining sufficient low-level analysis and granularity to be able to identify confluences of factors that can result in rare events. The scaling optimizer  2630  has one or more components that reduce that scale of analyses to a tractable level with finite computing resources, including a dimensionality reducer  2631 , a micro-scale optimizer  2632 , and a macro-scale optimizer  2633 . The dimensionality reducer  2631  is used to limit the scope of the problem under analysis either by selecting certain features for analysis or by combining a large set of variables into a smaller set of variables that are combinations of the large set of variables containing essentially the same information. The dimensionality reducer  2631  may rely on techniques such as sliding time windows, filters and algorithms such as missing value ratios, low variance filters, high correlation filers, etc., or reinforcement learning algorithms such as genetic algorithms and stochastic scheduling strategies to reduce the dimensionality of the analyses to a tractable range. The micro-scale optimizer  2632  may be used to determine the right “balance” between perturbations and iterative cycles of a particular system model  2610  or of sub-systems within a particular system model  2610  before enabling cyber-physical model interactions of a larger set of system models  2610 . An example of this methodology is fluid-structure interaction (FSI) analysis where an independent model evolution is evaluated within discrete time steps based on the amount of sensitivity impact to overall outcomes against some defined objective function. A macro-scale optimizer  2633  may be used to determine when new simulations of the system models  2610  should be triggered. New simulations may be triggered, for example, by determining what degree of change in state or objective function should trigger new simulations based on the economic, time, or computing resources cost estimates of simulations versus the value and actionability of potential information gains by conducing new simulations. The frequency of such changes in state may be monitored, and used to trigger a new simulation when the threshold degree of change in state would be expected to occur, even if it is not determined that the threshold degree of change has actually occurred. 
       FIG. 30  is a block diagram showing an exemplary system architecture for an automated cybersecurity defensive strategy analysis and recommendation system. In this embodiment, the system is structured to provide an iterative analysis and improvement process that uses an attack implementation engine  3010  to test an actual network under test  3001 , gathers system information from the test, which is used by a simulator  3100  to initiate an iterative simulation of a cyberattack strategy sequence, with each iteration comprising a simulated attack generated by a machine learning algorithm on a model of the network under test  3001  and a simulated defense generated by a machine learning algorithm against the simulated attack. The iterative simulation produces a simulation result, which is passed to a recommendation engine  3200 , winch recommends cybersecurity improvements to the network under test  3001  based on one or more cost factors and benefit factors. The improvements are implemented on the network under test  3001 , which starts the next iteration of testing. A test may be initiated either manually by an administrator  3005  or automatically by a recommendation engine  3200 , with the test initiation describing an attack strategy to be tested. Upon receiving test initiation instructions, an action planning engine  3011  determines how to implement the attack strategy by determining a set of action decisions. The action planning engine  3011  may be a state/action engine wherein possible courses of action are determined by states of the system and the actions possible from that state, and may use a state/action modeling language such as Action Notation Modeling Language (ANML), although other implementations are possible. The action decisions from the action planning engine  3011  are passed to a distributed computational graph  3012 , which contains detailed workflows for implementing cyberattacks on the network under test  3001  based on the action decisions. The workflows in the distributed computational graph  3012  are used to control attacks generated by an offensive tool microservice  3013  which contains a collection of cyberattack tools joined together by a scheduler or script engine  3014 , which defines when each cyberattack tool will be used against the network under test  3001 . The network under test  3001  is a real-world network that may be an entire network or a subset of an entire network. The network under test  3001  may be an actual network in live operation, or may be a network created specifically for testing purposes and not associated with live operations. Portions of the network under test  3001  may be sandboxed to prevent damage to, or infiltration of, other areas of the network, particularly where the network under test  3001  is an actual network in live operation. 
     During testing, the network under test  3001  is monitored to capon e system information about the operation f the network under test  3001  during the test cyberattack, including time series information about the sequence of events and response of affected devices. In this example, system log (syslog) information and time series data is gathered from affected devices and sent to a syslog parser  3002  which sorts the system logs and associates them i time events to create a, time series data store  3003  of the cyberattack and the network&#39;s response. 
     The real-world data from the time series data store  3003  is used to inform the operations of a simulator  3100 , which uses the real-world data to run attack and defense simulations using a machine learning engine  3120 , which uses machine learning algorithms such as reinforcement learning or evolutionary learning algorithms to test a model of the network under test  3001  represented by a cyber-physical graph  3101 . The results of the simulations comprise a probability of success of various attack and defense strategies arising out of the implementation of the real-world attack data on the model of the network under test  3001  represented by the cyber-physical graph  3101 . The simulation results from the simulator  3100  are sent to a, recommendation engine  3200 , which compares the likelihood of success of the various attack and defense strategies against real-world cost and benefit considerations to generate recommendations for cost-effective, realistic security improvements to the network under test  3001 . The recommendations may be used to automatically implement security improvements and initiate the next iteration of testing. The recommendations are also sent to an administrative user interface  3004 , which may be used by an administrator  3005  to manually implement security improvements and initiate the next iteration of testing. 
     Detailed Description of Exemplary Aspects 
       FIG. 8  is a flow diagram of an exemplary method  800  for cybersecurity behavioral analytics, according to one aspect. According to the aspect, behavior analytics may utilize passive information feeds from a plurality of existing endpoints (for example, including but not limited to user activity on a network, network performance, or device behavior) to generate security solutions. In an initial step  801 , a web crawler  115  may passively collect activity information, which may then be processed  802  using a DCG  155  to analyze behavior patterns. Based on this initial analysis, anomalous behavior may be recognized  803  (for example, based on a threshold of variance from an established pattern or trend) such as high-risk users or malicious software operators such as bots. These anomalous behaviors may then be used  804  to analyze potential angles of attack and then produce  805  security suggestions based on this second-level analysis and predictions generated by an action outcome simulation module  125  to determine the likely effects of the change. The suggested behaviors may then be automatically implemented  806  as needed. Passive monitoring  801  then continues, collecting information after new security solutions are implemented  806 , enabling machine learning to improve operation over time as the relationship between security changes and observed behaviors and threats are observed and analyzed. 
     This method  800  for behavioral analytics enables proactive and high-speed reactive defense capabilities against a variety of cyberattack threats, including anomalous human behaviors as well as nonhuman “bad actors” such as automated software bots that may probe for, and then exploit, existing vulnerabilities. Using automated behavioral learning in this manner provides a much more responsive solution than manual intervention, enabling rapid response to threats to mitigate any potential impact, Utilizing machine learning behavior further enhances this approach, providing additional proactive behavior that is not possible in simple automated approaches that merely react to threats as they occur. 
       FIG. 9  is a flow diagram of an exemplary method  900  for measuring the effects of cybersecurity attacks, according to one aspect. According to the aspect, impact assessment of an attack may be measured using a DCG  155  to analyze a user account and identify its access capabilities  901  (for example, what files, directories, devices or domains an account may have access to). This may then be used to generate  902  an impact assessment score for the account, representing the potential risk should that account be compromised. In the event of an incident, the impact assessment score for any compromised accounts may be used to produce a “blast radius” calculation  903 , identifying exactly what resources are at risk as a result of the intrusion and where security personnel should focus their attention. To provide proactive security recommendations through a simulation module  125 , simulated intrusions may be run  904  to identify potential blast radius calculations for a variety of attacks and to determine  905  high risk accounts or resources so that security may be improved in those key areas rather than focusing on reactive solutions. 
       FIG. 10  is a flow diagram of an exemplary method  1000  for continuous cybersecurity monitoring and exploration, according to one aspect. According to the aspect, a state observation service  140  may receive data horn a variety of connected systems  1001  such as (for example, including but not limited to) servers, domains, databases, or user directories. This information may be received continuously, passively collecting events and monitoring activity over time while feeding  1002  collected information into a graphing service  145  for use in producing time series graphs  1003  of states and changes over time. This collated time series data may then be used to produce a visualization  1004  of changes over time, quantifying collected data into a meaningful and understandable format. As new events are recorded, such as changing user roles or permissions, modifying servers or data structures, or other changes within a security infrastructure, these events are automatically incorporated into the time series data, and visualizations are updated accordingly, providing live monitoring of a wealth of information in a way that highlights meaningful data without losing detail due to the quantity of data points under examination. 
       FIG. 11  is a flow diagram of an exemplary method  1100  for mapping a cyber-physical system graph (CPC), according to one aspect. According to the aspect, a cyber-physical system graph may comprise a visualization of hierarchies and relationships between devices and resources in a security infrastructure, contextualizing security information with physical device relationships that are easily understandable for security personnel and users. In an initial step  1101 , behavior analytics information (as described previously, referring to  FIG. 8 ) may be received at a graphing service  145  for inclusion in a CPG. In a next step  1102 , impact assessment scores (as described previously, referring to  FIG. 9 ) may be received and incorporated in the CPG information, adding risk assessment context to the behavior information. In a next step  1103 , time series information (as described previously, referring to  FIG. 10 ) may be received and incorporated, updating CPG information as changes occur and events are logged. This information may then be used to produce  1104  a graph visualization of users, servers, devices, and other resources correlating physical relationships (such as a user&#39;s personal computer or smartphone, or physical connections between servers) with logical relationships (such as access privileges or database connections), to produce a meaningful and contextualized visualization of a security infrastructure that reflects the current state of the internal relationships present in the infrastructure. 
       FIG. 12  is a flow diagram of an exemplary method  1200  for continuous network resilience rating, according to one aspect. According to the aspect, a baseline score can be used to measure an overall level of risk for a network infrastructure, and may be compiled by first collecting  1201  information on publicly-disclosed vulnerabilities, such as (for example) using the Internet or common vulnerabilities and exploits (CVE) process. This information may then  1202  be incorporated into a CPG as described previously in  FIG. 11 , and the combined data of the CPG and the known vulnerabilities may then be analyzed  1203  to identify the relationships between known vulnerabilities and risks exposed by components of the infrastructure. This produces a combined CPG  1204  that incorporates both the internal risk level of network resources, user accounts, and devices as well as the actual risk level based on the analysis of known vulnerabilities and senility risks. 
       FIG. 13  is a flow diagram of an exemplary method  1300  for cybersecurity privilege oversight, according to one aspect. According to the aspect, time series data (as described above, referring to  FIG. 10 ) may be collected  1301  for user accounts, credentials, directories, and other user-based privilege and access information. This data may then  1302  be analyzed to identify changes over time that may affect security, such as modifying user access privileges or adding new users. The results of analysis may be checked  1303  against a CPG (as described previously in  FIG. 11 ), to compare and correlate user directory changes with the actual infrastructure state. This comparison may be used to perform accurate and context-enhanced user directory audits  1304  that identify not only current user credentials and other user-specific information, but changes to this information over time and how the user information relates to the actual infrastructure (for example, credentials that grant access to devices and may therefore implicitly grant additional access clue to device relationships that were not immediately apparent from the user directory alone). 
       FIG. 14  is a flow diagram of an exemplary method  1400  for cybersecurity risk management according to one aspect. According to the aspect, multiple methods described previously may be combined to provide live assessment of attacks as they occur, by first receiving  1401  time series data for an infrastructure (as described previously, in  FIG. 10 ) to provide live monitoring of network events. This data is then enhanced  1402  with a CPG (as described above in  FIG. 11 ) to correlate events with actual infrastructure elements, such as servers or accounts. When an event (for example, an attempted attack against a vulnerable system or resource) occurs  1403 , the event is logged in the time series data  1404 , and compared against the CPG  1405  to determine the impact. This is enhanced with the inclusion of impact assessment information  1406  for any affected resources, and the attack is then checked against a baseline score  1407  to determine the full extent of the impact of the attack and any necessary modifications to the infrastructure or policies. 
       FIG. 15  is a flow diagram of an exemplary method  1500  for mitigating compromised credential threats, according to one aspect. According to the aspect, impact assessment scores (as described previously, referring to  FIG. 9 ) may be collected  1501  for user accounts in a directory, so that the potential impact of any given credential attack is known in advance of an actual attack event. This information may be combined with a CPG  1502  as described previously in  FIG. 11 , to contextualize impact assessment scores within the infrastructure (for example, so that it may be predicted what systems or resources might be at risk for any given credential attack). A simulated attack may then be performed  1503  to use machine learning to improve security without waiting for actual attacks to trigger a reactive response. A blast radius assessment (as described above in  FIG. 9 ) may be used in response  1504  to determine the effects of the simulated attack and identify points of weakness, and produce a recommendation report  1505  for improving and hardening the infrastructure against future attacks. 
       FIG. 16  is a flow diagram of an exemplary method  1600  for dynamic network and rogue device discovery, according to one aspect. According to the aspect, an advanced cyber decision platform may continuously monitor a network in real-time  1601 , detecting any changes as they occur. When a new connection is detected  1602 , a CPG may be updated  1603  with the new connection information, which may then be compared against the network&#39;s resiliency score  1604  to examine for potential risk, The blast radius metric for any other devices involved in the connection may also be checked  1605 , to examine the context of the connection for risk potential (for example, an unknown connection to an internal data server with sensitive information may be considered a much higher risk than an unknown connection to an externally-facing web server). If the connection is a risk, an alert may be sent to an administrator  1606  with the contextual information for the connection to provide a concise notification of relevant details for quick handling. 
       FIG. 17  is a flow diagram of an exemplary method  1700  for Kerberos “golden ticket” attack detection, according to one aspect. Kerberos is a network authentication protocol employed across many enterprise networks to enable single sign-on and authentication for enterprise services. This makes it an attractive target for attacks, which can result in persistent, undetected access to services within a network in what is known as a “golden ticket” attack. To detect this form of attack, behavioral analytics may be employed to detect forged authentication tickets resulting from an attack. According to the aspect, an advanced cyber decision platform may continuously monitor a network  1701 , informing a CPG in real-time of all traffic associated with entities in an organization, for example, people, places, devices, or services  1702 . Machine learning algorithms detect behavioral anomalies as they occur in real-time  1703 , notifying administrators with an assessment of the anomalous event  1704  as well as a blast radius score for the particular event and a network resiliency score to advise of the overall health of the network. By automatically detecting unusual behavior and informing an administrator of the anomaly along with contextual information for the event and network, a compromised ticket is immediately detected when a new authentication connection is made. 
       FIG. 18  is a flow diagram of an exemplary method  1800  for risk-based vulnerability and patch management, according to one aspect. According to the aspect, an advanced cyber decision platform may monitor all information about a network  1801 , including (but not limited to) device telemetry data, log files, connections and network events, deployed software versions, or contextual user activity information. This information incorporated into a CPG  1802  to maintain an up-to-date model of the network in real-time. When a new vulnerability is discovered, a blast radius score may be assessed  1803  and the network&#39;s resiliency score may be updated  1804  as needed. A security alert may then be produced  1805  to notify an administrator of the vulnerability and its impact, and a proposed patch may be presented  1806  along with the predicted effects of the patch on the vulnerability&#39;s blast radius and the overall network resiliency score. This determines both the total impact risk of any particular vulnerability, as well as the overall effect of each vulnerability on the network as a whole. This continuous network assessment may be used to collect information about new vulnerabilities and exploits to provide proactive solutions with clear result predictions, before attacks occur. 
       FIG. 20  is a relational diagram showing the relationships between exemplary 3 rd  party search tools  1915 , search tasks  2010  that can be generated using such tools, and the types of information that may be gathered with those tasks  2011 - 2014 , and how a public-facing proxy network  1908  may be used to influence the search task results, the use of 3 rd  party search tools  1915  is in no way required, and proprietary or other self-developed search tools may be used, there are numerous 3 rd  party search tools  1915  available on the Internet, many of them available for use free of charge, that are convenient for purposes of performing external and internal reconnaissance of an organization&#39; s infrastructure. Because they are well-known, they are included here as examples of the types of search tools that may be used and the reconnaissance data that may be gathered using such tools. The search tasks  2010  that may be generated may be classified into several categories. While this category list is by no means exhaustive, several important categories of reconnaissance data are domain and internet protocol (IP) address searching tasks  2011 , corporate information searching tasks  2012 , data breach searching tasks  2013 , and dark web searching tasks  2014 . Third party search tools  1915  for domain and IP address searching tasks  2011  include, for example, DNSDumpster, Spiderfoot HX, Shodan, VirusTotal, Dig, Censys, ViewDNS, and CheckDMARC, among others. These tools may be used to obtain reconnaissance data about an organization&#39;s server IPs, software, geolocation; open ports, patch/setting vulnerabilities; data hosting services, among other data  2031 . Third party search tools  1915  for corporate information searching tasks  2012  include, for example, Bloomberg.com, Wikipedia, SEC.gov, AnnualReportsocom, DNB.com, Hunter.io, and MarketVisual, among others. These tools may be used to obtain reconnaissance data about an organization&#39;s addresses; corp info; high value target (key employee or key data assets) lists, emails, phone numbers, online presence  2032 . Third party search tools  1915  for data breach searching tasks  2013  include, for example, DeHashed, WeLeakInfo, Pastebin, Spiderfoot, and BreachCompilation, among others. These tools may be used to obtain reconnaissance data about an organization&#39;s previous data breaches, especially those involving high value targets, and similar data loss information  2033 . Third party search tools  1915  for deep web (reports, records, and other documents linked to in web pages, but not indexed in search results . . . estimated to be 90% of available web content) and dark web (websites accessible only through anonymnizers such as TOR . . . estimated to be about 6% of available web content) searching tasks  2013  include, for example, Pipl, MyLife, Yippy, SurfWax, Wayback machine, Google Scholar, DuckDuckGo, Fazzle, Not Evil, and Start Page, among others. These tools may be used to obtain reconnaissance data about an organization&#39;s lost and stolen data such as customer credit card numbers, stolen subscription credentials, hacked accounts, software tools designed for certain exploits, which organizations are being targeted for certain attacks, and similar information  2034 . A public-facing proxy network  1908  may be used to change the outward presentation of the organization&#39;s network by conducting the searches through selectable attribution nodes  2021   a - n,  which are configurable to present the network to the Internet in different ways such as, but not limited to, presenting the organization network as a commercial IP address, a residential IP address, or as an IP address from a particular country, all of which may influence the reconnaissance data received using certain search tools. 
       FIG. 21  is a relational diagram showing the exemplary types and classifications of information that may be used in constructing a cyber-physical graph  1902  of an organization&#39;s infrastructure and operations. The cyber-physical graph  1902  is a directed graph that represents a comprehensive picture of an organization&#39;s infrastructure and operations. A cyber-physical graph  1902  represents the relationships between entities associated with an organization, for example, devices, users, resources, groups, and computing services, the relationships between the entities defining relationships and processes in an organization&#39;s infrastructure, thereby contextualizing security information with physical and logical relationships that represent the flow of data and access to data within the organization including, in particular, network security protocols and procedures. Data that may be incorporated into a cyber-physical graph may be any data relating to an organization&#39;s infrastructure and operations, and two primary categories of data that may be incorporated are internal reconnaissance data  2110  and external reconnaissance data  2120 . Non-limiting examples of internal reconnaissance data  2110  include computers and devices, physical and intangible (data) assets, people (employees, contractors, etc.), addresses and locations of buildings, servers, etc., business processes, access privileges, loss information, legal documents, and self-assessments of cybersecurity. Non-limiting examples of external reconnaissance data  2120  include domains and IP information, data breach information, organization information such as corporate structures, key employees, etc., open port information, information regarding which organizations are current targets of cyber-attacks, network vulnerability information, system version and patch/update information, known and possible exploits, and publicly available information. 
     In an initial step  1101 , behavior analytics information (as described previously, referring to  FIG. 8 ) may be received at a graphing service  145  for inclusion in a CPG. In a next step  1102 , impact assessment scores (as described previously, referring to  FIG. 9 ) may be received and incorporated in the CPG information, adding risk assessment context to the behavior information. In a next step  1103 , time series information (as described previously, referring to  FIG. 10 ) may be received and incorporated, updating CPG information as changes occur and events are logged. This information may then be used to produce  1104  a graph visualization of users, servers, devices, and other resources correlating physical relationships (such as a user&#39;s personal computer or smartphone, or physical connections between servers) with logical relationships (such as access privileges or database connections), to produce a meaningful and contextualized visualization of a security infrastructure that reflects the current state of the internal relationships present in the infrastructure. 
       FIG. 22  is a directed graph diagram showing an exemplary cyber-physical graph  2200  and its possible use in creating cybersecurity profiles and ratings. A cyber-physical graph  1902  represents the relationships between entities associated with an organization, for example, devices, users, resources, groups, and computing services, the relationships between the entities defining relationships and processes in an organization&#39;s infrastructure, thereby contextualizing security information with physical and logical relationships that represent the flow of data and access to data within the organization including, in particular, network security protocols and procedures. A cyber-physical graph, in its most basic form, represents the network devices comprising an organization&#39;s network infrastructure as nodes (also called vertices) in the graph and the physical or logical connections between them as edges between the nodes. The cyber-physical graph may be expanded to include network information and processes such as data flow, security protocols and procedures, and software versions and patch information. Further, human users and their access privileges to devices and assets may be included. A cyber-security graph may be further expanded to include internal process information such as business processes, loss information, and legal requirements and documents; external information such as domain and IP information, data breach information; and generated information such as open port information from external network scans, and vulnerabilities and avenues of attack. Thus, a cyber-physical graph may be used to represent a complete picture of an organization&#39;s infrastructure and operations. 
     In this example, which is necessarily simplified for clarity, the cyber-physical graph  2200  contains 12 nodes (vertices) comprising: seven computers and devices designated by solid circles  2202 ,  2203 ,  2204 ,  2206 ,  2207 ,  2209 ,  2210 , two users designated by dashed-line circles  2201 ,  2211 , and three functional groups designated by dotted-line circles  2205 ,  2208 , and  2212 . The edges (lines) between the nodes indicate relationships between the nodes, and have a direction and relationship indicator such as “AdminTo,” “MearberOf,” etc. While not shown here, the edges may also be assigned numerical weights or probabilities, indicating, for example, the likelihood of a successful attack gaining access from one node to another. Possible attack paths may be analyzed using the cyber-physical graph by running graph analysis algorithms such as shortest path algorithms, minimum cost/maximum flow algorithms, strongly connected node algorithms etc. In this example, several exemplary attack paths are ranked by likelihood. In the most likely attack path, user  2201  is an administrator to device  2202  to which device  2203  has connected. Device  2203  is a member of functional group  2208 , which has a member of group  2212 . Functional group  2212  is an administrator to the target  2206 . In a second most likely attack path, user  2201  is an administrator to device  2207  to which device  2204  has connected. Device  2204  is a member of functional group  2205 , which is an administrator to the target device  2206 . In a third most likely attack path, a flaw in the security protocols allow the credentials of user  2201  to be used to gain access to device  2210 . User  2211  who is working on device  2210  may be tricked into providing access to functional group  2205 , which is an administrator to the target device  2206 . 
       FIG. 23  is a block diagram showing exemplary operation of a data to rule mapper. Laws, policies, standards, and other rules are gathered and stored in an authority database  1903 . Non-limiting examples of such rules include federal, state, and local statutes, regulations, case law interpretations, and other laws  2310 , business policies and procedures  2320 , and industry standards (as one example, cybersecurity industry standards for network security)  2330 . Reconnaissance data are stored in a database  1905 . A data to rule mapper  1904  retrieves the reconnaissance data  1905  and matches it to rules from the authority database  1903 . An example of this operation for statues/regulations  2310  is shown in  2311 , where Article 33, paragraph 1 of the European Union&#39;s General Data Protection Regulation (GDPR) requires that an organization notify a cognizant authority of a data breach within 72 hours of knowledge of the breach. If a data point indicates that a data breach has been discovered because data of the organization is found online, the data point is associated with that rule, and tagged with the possible impact of fines if the rule is not followed. An example of this operation for business policies  2320  is shown in  2321 , where a corporate policy prohibits access of the organization&#39;s systems using personal computers. If a data point indicates that an employee account is accessed using a non-business-owned computer, the data point is associated with the rule, and tagged with possible data theft and/or security breach. An example of this operation for industry standards  2330  is shown in  2331 , where an industry standard prohibits open ports accessible from outside the network perimeter. If a data point indicates an open port, the data point is associated with the rule, and tagged with possible data loss and/or security breach. 
       FIG. 27  is a directed graph diagram showing air example of the use of a cyber-physical graph to model a simple salinity adjustment process control system  2700 . This example is a simple example for clarity and understandability, and does not limit the types of systems that may be modeled using this methodology. In this cyber-physical graph, nodes (aka vertices) represent entities (in this case components and devices) and the edges between the nodes represent logical relationships between the nodes. In this case, the system is controlled by a programmable logic controller (PLC)  2701 . The upper half of the graph represents the flow of a source fluid, and the lower half of the graph represents the flow of a concentrated saline solution used to ensure that the salinity of the outflow from the system is meets or exceeds a threshold salinity. The source fluid is contained in a tank  2705 . The salinity of the source. fluid is monitored by a sensor  2702 , which reports the salinity data to the PLC  2701 . The source fluid flows to a pump  2706 , the motor of which is controlled by a motor controller  2708  using signals from the PLC  2701  based on pump speed data sent from a pump speed sensor  2703 . The pump pushes the source fluid at a constant pressure to a valve  2707 , which is controlled by an actuator  2709  using signals from the PLC  2701  based on flow rate data sent by a flow sensor  2704 . The source fluid flows from the valve  2707  to a mixing tank  2711 , the salinity of which is measured by a sensor  2710  and the salinity data for which is sent to the PLC  2710 . Similarly, in the lower half of the graph, the saline solution fluid is contained in a tank  2714 . The salinity of the saline solution fluid is monitored by a sensor  2717 , which reports the salinity data to the PLC  2701 . The saline solution fluid flows to a pump  2715 , the motor of which is controlled by a motor controller  2712  using signals from the PLC  2701  based on pump speed data sent from a pump speed sensor  2718 . The pump pushes the saline solution fluid at a constant pressure to a valve  2716 , which is controlled by an actuator  2713  using signals from the PLC  2701  based on flow rate data sent by a flow sensor  2719 . The saline solution fluid flows from the valve  2716  to a mixing tank  2711 , the salinity of which is measured by a sensor  2710  and the salinity data for which is sent to the PLC  2710 . Based on the salinity and flow rates of the fluids, the salinity of the outflow from the mixing tank  2711  can be guaranteed to meet or exceed a certain threshold. While not shown in this diagram, it is possible to incorporate time series data into a cyber-physical graph of an operational technology system to create a hybrid time series/graph model using the methods shown and described in  FIG. 29 , wherein data are captured using individual time series swimlanes that are optionally referenced by nodes and edges in the graph to capture the additional state information of the system. For example, nodes in the graph may reference the particular sensor readings on devices represented by the nodes, and edges between the nodes may reference the actual commands between devices, such as commands from the PLC  2701  to the motor controller  2708 , and may further store this additional stare information as a time series (i.e., a history of readings and commands sent over time).  FIG. 28  is a method diagram showing how parametric analysis of integrated operational technology and information technology systems may be employed to detect cybersecurity threats. In a first step, parametric analyses are run of sensors in the OT system model  2801 . A baseline behavior of both the OT and IT system models is determined in response to the parametric analyses  2802 . Sensor parameters which might indicate control by an unauthorized entity are identified  2803 , and the behavior of the OT and IT systems at those parameter points are determined  2804 . The real-world OT and IT systems on which the models are based are monitored  2805 , and if behaviors similar to those from the models are discovered, such behaviors are flagged as possibly indicating control by an unauthorized entity  2806 . 
       FIG. 29  is a diagram of an exemplary architecture of a system for the capture and storage of time series data from sensors with heterogeneous reporting profiles according to an embodiment of the invention  2900 . In this embodiment, a plurality of sensor devices  2910   a - n  stream data to a collection device, in this case a web server acting as a network gateway  2915 . These sensors  2910   a - n  can be of several forms, some non-exhaustive examples being: physical sensors measuring humidity, pressure, temperature, orientation, and presence of a gas; or virtual such as programming measuring a level of network traffic, memory usage in a controller, and number of times the word “refill” is used in a stream of email messages on a particular network segment, to name a small few of the many diverse forms known to the art. In the embodiment, the sensor data is passed without transformation to the data management engine  2920 , where it is aggregated and organized for storage in a specific type of data store  2925  designed to handle the multidimensional time series data, resultant from sensor data. Raw sensor data can exhibit highly different delivery characteristics. Some sensor sets may deliver low to moderate volumes of data continuously. It would be infeasible to attempt to store the data in this continuous fashion to a data store, as attempting to assign identifying keys to store real tune data from large volumes of continuously-streaming data from multiple sensors would invariably lead to significant data loss. In this circumstance, the data stream management engine  2920  would hold incoming data in memory, keeping only the parameters, or “dimensions” from within the larger sensor stream that are pre-decided by the administrator of the study as important and instructions to store them transmitted from the administration device  2912 . The data stream management engine  2920  would then aggregate the data from multiple individual sensors and apportion that data at a predetermined interval, for example, every 10 seconds, using the timestamp as the key when storing the data to a multidimensional time series data store over a single swimlane of sufficient size. This highly ordered delivery of a foreseeable amount of data per unit time is particularly amenable to data capture and storage but patterns where delivery of data from sensors occurs irregularly and the amount of data is extremely heterogeneous are quite prevalent. In these situations, the data stream management engine cannot successfully use strictly single time interval over a single swimlane mode of data storage. In addition to the single time interval method the invention also can make use of event based storage triggers where a predetermined number of data receipt events, as set at the administration device  2912 , triggers transfer of a data block consisting of the apportioned number of events as one dimension and a number of sensor ids as the other. In the embodiment, the system time at commitment or a time stamp that is part of the sensor data received is used as the key for the data block value of the value-key pair. The invention can also accept a raw data stream with commitment occurring when the accumulated stream data reaches a predesigned size set at the administration device  2912 . 
     It is also likely that that during times of heavy reporting from a moderate to large array of sensors, the instantaneous load of data to be committed will exceed what can be reliably transferred over a single swimlane. The embodiment of the invention can, if capture parameters pre-set at the administration device  2912 , combine the data movement, capacity of two or more swimlanes, the combined bandwidth dubbed a metaswimlane, transparently to the committing process, to accommodate the influx of data in need of commitment. All sensor data, regardless of delivery circumstances are stored in a multidimensional time series data store  2925  which is designed for very low overhead and rapid data storage and minimal maintenance needs to sap resources. The embodiment uses a key-value pair data store examples of which are Riak, Redis and Berkeley DB for their low overhead and speed, although the invention is not specifically tied to a single data store type to the exclusion of others known in the art should another data store with better response and feature characteristics emerge. Data store commitment reliability is dependent on data store data size under the conditions intrinsic to time series sensor data analysis. The number of data records must be kept relatively low for the herein disclosed purpose. As an example, one group of developers restrict the size of their multidimensional time series key-value pair data store to approximately 8.64×20 4  records, equivalent to 24 hours of 1 second interval sensor readings or 60 days of 1 minute interval readings. In this development system the oldest data is deleted from the data store and lost. This loss of data is acceptable under development conditions but in a production environment, the loss of the older data is almost always significant and unacceptable. The invention accounts for this need to retain older data by stipulating that aged data be placed in long term storage. In the embodiment, the archival storage is included  2930 . This archival storage might be locally provided by the user, might be cloud based such as that offered by Amazon Web Services or Google or could be any other available very large capacity storage method known to those skilled in the art. 
     Reliably capturing and storing sensor data as well as providing for longer term, offline, storage of the data, while important, is only an exercise without methods to repetitively retrieve and analyze most likely differing but specific sets of data over time. The invention provides for this requirement with a robust query language that both provides straightforward language to retrieve data sets bounded by multiple parameters, but to then invoke several transformations on that data set prior to output. In the embodiment isolation of desired data sets and transformations applied to that data occurs using pre-defined query commands issued from the administration device  2912  and acted upon within the database by the structured query interpreter  2935 . Below is a highly simplified example statement to illustrate the method by which a very small number of options that are available using the structured query interpreter  2935  might be accessed: 
     SELECT [STREAMING|EVENTS] data_spec FROM [unit] timestamp TO timestamp GROUPBY (sensor_id, identifier) FILTER [filter_identifier] FORMAT [sensor [AS identifier] [, sensor [AS identifier]] . . . ] (TEXT|JSON|FUNNEL|KML|GEOJSON|TOPOJSON). In this example, “data_spec” might be replaced by a list of individual sensors from a larger array of sensors and each sensor in the list might be given a human readable identifier in the format “sensor AS identifier”, “unit” allows the researcher to assign a periodicity for the sensor data such as second (s), minute (m), hour (h). One or more transformational filters, which include but a not limited to: mean, median, variance, standard deviation, standard linear interpolation, or Kalman filtering and smoothing, may be applied and then data formatted in one oi more formats examples of with are text, JSON, KML, GEOJSON and TOPOJSON among others, depending on the intended use of the data. The results of the structured query may be passed to other systems using an output engine  2940 . 
       FIG. 31  is a block diagram showing an exemplary system architecture for a machine learning simulator  3100  for an automated cybersecurity defensive strategy analysis and recommendation system. The network for simulations is modeled by a cyber-physical graph  3101 , details regarding the use of which are described in preceding paragraphs. In this embodiment, the machine learning simulator  3100  pits a reinforcement learning (RL) attack engine  3122  against a dynamically-modified network model driven by an evolutionary algorithm (EA) defense engine. EA algorithms are a type of machine learning algorithm similar to genetic algorithms, in that they continually evolve their operation based on the current state plus possible improvements which are tested in the next iteration, with the most successful variants passing on their traits to the next “generation” of improvements. 
     A generative modeling attack generator  8104  simulates an attacker using available attack building resources  3102  which contain scripts for implementing certain types of attacks on a network. For example, one attack building resource might be a port scanner that scans the network for open ports. Another might be a system version checker that checks the version of services running on the open ports to find unpatched services that are vulnerable to exploits. A reinforcement learning (RL) attack engine applies the generated attacks to the network model through a real-time simulation engine  3105 , and learns which attacks are most effective by gaining a reward for each successful attack. The attack building resources  3102  may be updated by the RL attack engine as new forms of possible attacks find success. As part of its reinforcement learning process, the RL attack engine  3103  calculates probabilities of success of certain attack strategies based on outcome of attacks presented to the real-time simulation engine  3105 . 
     An evolutionary algorithm (EA) defense engine  3106  is used to generate defenses against cyberattacks for the network model represented by the cyber-physical graph  3001  by making evolutionary changes to the state of the cyber-physical graph  3001  and determining which evolution paths are most effective against the attacks presented by the RL attack engine  3103 . In developing its evolutionary changes to the state of the network model, the EA defense engine  3106  uses available defense building resources  3107  which contain scripts for implementing certain types of defenses on a network. An example of a defense building resource might be a port scanner that checks for and closes open ports. Another might be an updater that checks software and services running on the network and installs updates and patches. The defense building resources  3107  may be updated by the EA defense engine  3124  applying a suitable fitness function characterizing network resilience as new forms of possible defense are discovered. Thus, the state of the network model is dynamically evolved through generations of defensive changes to the network state in response to the attacks presented. As part of the determination of the success of evolutionary defensive paths, the EA defense engine  3106  calculates probabilities of success of certain defensive strategies based on outcome of defensive state changes made to the cyber-physical graph  3101  and processed through the real-time simulation engine  3105  in response to attacks presented by the RL attack engine  3103 . 
     The attack and defense strategies generated by the attack engine  3105  are run on a real-time simulation engine, which implements the attacks on the cyber-physical graph and measures the impact of mitigations and other features of network resilience. The real-time simulation engine further captures the probabilities of successful attack strategies from the RL attack engine  3103  and the probabilities of successful defense strategies from the EA defense engine  3106 , and outputs simulation results comprising those probabilities 
     In some embodiments, the simulation may be hosted online, and outside participants may be invited to participate in the cyberattack simulations. In such a situation, human participants may develop attack and/or defense strategies, as in the form of a real-time simulation game, or may participate by developing automated machine learning algorithms that develop attack and/or defense strategies, such as continual online evolutionary planning (COEP) algorithms. COEP algorithms are a form of evolutionary algorithms that have been adapted to run on persistent online real-time simulation games and are used to plan development and use of in-game resources. 
       FIG. 32  is a block diagram showing exemplary inputs to a recommendation engine  3200  for an automated cybersecurity defensive strategy analysis and recommendation system. The recommendation engine  3200  receives simulation results, performs a cost/benefit analysis, and makes recommendations as to what security improvements to implement. The simulation results comprise probabilities of the success of attack and defense strategies based on a model of the network under test. However, the probabilities of success are only one factor to be considered in an analysis of which security improvements to implement. The costs of implementation  3202   a - n  and the technical difficulty and benefits to be gained  3203   a - n  need to be considered, as well. For example, it may be that a certain attack strategy is very likely to succeed, but a successful attack gains access only to information that is already publicly available, in which case minimal or no expenditures are justified in defending against that attack strategy. Conversely, a certain attack strategy may be very unlikely to succeed, requiring circumvention of a long chain of defenses, but a successful attack would allow the attacker control over the entire network, in which case large expenditures are justified in defending against that attack. A non-limiting list of cost factors to be considered  3202   a - n  is the cost of replacing or improving hardware components in the network  3202   a,  the personnel cost to program software or change configurations on the system  3202   b  to implement certain security measures, the cost to train personnel to change operation procedures  3202   c  to improve security, and the operational cost (including magnitude)  3202   n  if a successful attack occurs (e.g., the cost of lost productivity if the internal network is shut down, a distributed denial of service (DDoS) attack occurs preventing external access, the cost of data losses, etc.). A non-limiting list of technical difficulty and benefits  3203   a - n  is whether or not an attack is theoretically observable  3203   a  with existing network architecture (which impacts costs of upgrading the network), the limits of detectability (time scale, level of effort, expenditure of computing resources, etc.) of such an attack  3203   b  if an attack is theoretically observable, the ability of current defensive measures to respond to or mitigate the effects of an attack  3203   c,  the reduction in risk  3203   d  gained by implementing defensive measures against such an attack, and compliance impacts  3203   n  (i.e., does implementation of defensive measures against this attack reduce the effectiveness of defenses against other attacks, increase the risk in other areas, etc.) The weighting of these various factors against the probabilities in the simulation results will differ across organization types and across particular organizations within each type. For example, an organization in a labor-intensive industry like farming will have a substantially different risk profile than ail organization in a data-intensive industry like financial services. So, recommendations fora farming organization may differ from recommendations for a financial services organization, even for the same attack strategy. 
       FIG. 37  is a system diagram of a network-connected endpoint device operating malware detection agent software, according to an aspect. There is shown a block diagram depicting an exemplary computing device  3710  suitable for implementing at least a portion of the features or functionalities disclosed herein. Computing device  3710  may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory. Computing device  3710  may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired. 
     In one aspect, computing device  3710  includes one or more central processing units (CPU)  3712 , one or more interfaces  3715 , and one or more busses  3714  (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU  3712  may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one aspect, a computing device  3710  may be configured or designed to function as a server system utilizing CPU  3712 , local memory  3711  and or remote memory  3716 , and interface(s)  3715 . In at least one aspect, CPU  3712  may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like. 
     CPU  3712  may include one or more processors  3713  such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some aspects, processors  3713  may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device  3710 . In a particular aspect, a local memory  3711  (such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU  3712 . However, there are many different ways in which memory may be coupled to system  3710 . Memory  3711  may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU  3712  may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGONT™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices. 
     As used herein, the term “processor” is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application specific integrated circuit, and any other programmable circuit. 
     In one aspect, interfaces  3715  are provided as network interface cards (NICs). Generally, NICs control the sending and receiving of data packets over a computer network; other types of interfaces  3715  may for example support other peripherals used with computing device  3710 . Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL, interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRET™, THUNDERBOLIT™, PCI, parallel, radio frequency (RF), BLUETOOTH™, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP. ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces  3715  may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio of video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM). 
     In addition, in the memory  3720  of the endpoint computing device  3710 , there may be the data for the operation of an operating system (“OS”)  3721 , such as MICROSOFT WINDOWS™, MAC OS™, one of the distributions of LINUX™, or some other operating system; a possible plurality of applications  3722 ; a security agent  3723 ; and within the agent  3723 , there may exist software code for monitoring logic  3724  to monitor potential data packets and malware, filtering logic  3725  to filter out potentially malicious packets of data, and a classifier  3726  to classify the data as either malicious or non-malicious. Through a network interface  3717 , agent software  3723  operating on an endpoint, device  3710  may monitor either individual or grouped devices on a network, or the network traffic itself, or come combination thereof, for a network connected to the device  3710  by a network interface  3717 . Monitoring logic  3724  may dictate the operation of such an agent application  3723  so that it can capture packets of data transmitted through a network, between devices on a network, or directly to the endpoint  3710 , depending on the implementation, Packet capture and network sniffing may allow filtering logic  3725  and a classifier  3726  to filter through packets that may be deemed through other methodologies laid out, in the application to be potentially malicious, and classify them as such internally or relay such classification to other devices on a network the endpoint  3710  may be connected to. Specifically, the agent  3723  may capture the contextual direction of specific target packages (e.g. signatures, ports, and/or protocols to monitor and store raw data on, or run detections on) and then request or subscribe to publication of said telemetry in its raw form with or without any local processing results (e.g. PCAP2wav or other types of local file analysis/reassembly or signature analysis) on the local endpoint  3710  or a machine or machines operating a malware simulation. 
       FIG. 38  is a system diagram of a malware detection system, according to an aspect. A malware detection system (or “MDS”)  3800  may be connected to, or have internally, at least one network interface  3810 , and several software or hardware-implemented components comprising a static analysis logic engine  3820 , a dynamic analysis logic engine  3840 , scoring logic  3870 , a classifying engine  3880 , an indicator generator  3885 , and a security logic engine  3900 . A static analysis logic engine  3820 , a dynamic analysis logic engine  3840 , scoring logic  3870 , classifying engine  3880 , indicator generator  3885 , and security logic engine  3900 , may each separately or together be a separate hardware system or systems, such as a networked device or devices, or a software package or packages, application or applications, library or libraries, or component or components in another piece of software or hardware. A static analysis logic engine or module  3820  may contain two subcomponents—an indicator scanner  3830 , and a heuristic engine  3835 . An indicator scanner may scan packets of data and associated metadata, or search the packets themselves, for “indicator” data that may indicate the packet as part of malicious software. For instance, an indicator generator  3885  may eventually be tasked with developing a rule for a packet indicator, which can then be read and understood by the indicator scanner  3830  for quick and efficient malware data analysis. A heuristic engine  3835  may be operated to find non-generated indicators, or other non-generated data in packets, that may be indicative of malware, such as packets of data that are associated with each other being scanned and containing executable code for an operating system when the data should be an image file. In such cases and similar, an outright indicator may not be generated or detected, but the heuristic engine  3835  may be designed to detect such things from a static database, local memory or storage, or configuration files. 
     A dynamic analysis logic engine  3840  may contain several subcomponents including a scheduler  3850 , a software profile store or database  3855 , a virtual machine or machines  3860 , and an event database  3862 . A scheduler  3850  may schedule the operation and interplay between several other components in the MDS  3800 , including components internal to the dynamic analysis engine  3840 , or between the dynamic analysis logic  3840  and any or all of the static analysis logic  3820 , network interface or interfaces  3810 , and scoring logic  3870 . For example, the scheduler  3850  may dictate when packets of data are examined by the dynamic analysis engine  3840 . The software profile store  3855  may store configuration data and packet data or metadata about software packages that may be either malicious, or which are vulnerable to malware insertions or attacks, while a virtual machine or machines  3860  may simulate software operation and packet data reception to simulate the possible effects of any data received including malware operations, the results of which may be stored in an event database  3862 . 
     Scoring logic  3870  may be used to score specific packets that are detected as being of interest or potentially malicious based on the results of dynamic or static analyses  3820 ,  3840  winch may be specified manually by administrators or which may be programmatically specified by machine learning algorithms operating in the heuristic engine  3835  and event logic  3862  for determining how serious or fatal a given packet of data might be. The score is then classified with a classifying engine  3880  and sent to an indicator generator  3885  to develop an indicator that may be used to recognize similar data packets if it was not picked up initially by the static analysis  3820 , or which may be skipped over if it was already detected by the indicator scanner  3830 . In this way, both static analyses and dynamic testing and simulations may be performed to try to find malware being distributed over networks and between devices on networks, in an evolving and efficient manner. A security logic engine  3900  may be alerted by a reporting engine  3890  which may alert an external or internal security logic engine  3900 , and either additionally or alternatively forward the results of the analyses, scanning, scoring, and classifying, to other devices on the network or to network administrators, or performing some automated task such as sending an email, through a network interface  3810 . 
       FIG. 39  is a system diagram of a security logic engine, according to an aspect. A security logic engine  3900  contains at least a network interface or interfaces  3910 , formatting logic  3920  as software in the security logic engine, a correlation engine  3930 , a scorer  3940 , a classification engine  3950 , a labeler  3960 , a risk analyzer  3980 , an indicator scanner  3970 , and a reporting engine  3990 . A security logic engine  3900  is referred reports of malware or data packets captured over a network that may be related to malware, by a malware detection system  3800  as seen in  FIG. 38 , through the use of at least one network interface  3910 . A security logic engine  3900  may operate as part of the same physical computing system as the MDS  3800  or it, may operate as a separate endpoint on a network, as a separate computer or group of computers. When data is received by the MDS  3800 , a formatting logic engine  3920  may convert the data into a standardized format to be processed by other engines and logic in the engine  3900 , if not already in such a suitable format. The formatting logic  3920  may obtain data in disparate formats, which may be device specific or application specific, and transforms or converts it, into a consumable, common format. A correlation engine  3930  may correlate features of data packets received by the security logic engine  3900  from an endpoint device  3700  or malware detection system  3800  with previously-encountered or known behaviors and characteristics of software including malware, and it may further correlate behaviors seen from an endpoint device  3700  and behaviors seen and reported from a malware detection system  3800  to correlate the two and determine the accuracy of a malware detection or classification by the malware detection system  3800 . The results of the correlation may then be received by a scorer  3940 , which may then generate a score based on the correlations for an observed feature with known behaviors of various benign or malicious software data objects or packets. The classification engine  3950  then may utilize the scores generated by the scorer  3940  to classify a given captured packet or data object or software distribution as malicious if it exceeds a given threshold, which may be set manually or dynamically, either by users, the system itself, default settings, or some other method. A labeler  3960  may attribute a label or name of malware, or some other specifier or indicator, to malware if detected, which then may be sent to a risk analyzer  3980  to determine if it should be reported to other devices or users or networks, such as emailing company officials, alerting a malware detection system  3800 , warning an endpoint  3700  and its software of the detected malware, or some other reporting methodology, via a reporting engine  3990 . If malware needs to be reported, the reporting engine  3990  may send the data to an indicator scanner  3970  so that it can be more swiftly identified in future captured packets. 
     Hardware Architecture 
     Generally, the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card. 
     Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory. Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols. A general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented. According to specific aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof. In at least some aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments). 
     Referring now to  FIG. 33 , there is shown a block diagram depicting an exemplary computing device  10  suitable for implementing at least a portion of the features or functionalities disclosed herein. Computing device  10  may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory. Computing device  10  may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired. 
     In one aspect, computing device  10  includes one or more central processing units (CPU)  12 , one or more interfaces  15 , and one or more busses  14  (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU  12  may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one aspect, a computing device  10  may be configured or designed to function as a server system utilizing CPU  12 , local memory  11  and/or remote memory  16 , and interface(s)  15 . In at least one aspect, CPU  12  may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like. 
     CPU  12  may include one or more processors  13  such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some aspects, processors  13  may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device  10 . In a particular aspect, a local memory  11  (such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU  12 , However, there are many different ways in which memory may be coupled to system  10 . Memory  11  may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU  12  may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices. 
     As used herein, the term “processor” is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit. 
     In one aspect, interfaces  15  are provided as network interface cards (NICs). Generally, NICs control the sending and receiving of data packets over a computer network; other types of interfaces  15  may for example support other peripherals used with computing device  10 . Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radio frequency (RF), BLUETOOTH™, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces  15  may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM). 
     Although the system shown in  FIG. 33  illustrates one specific architecture for a computing device  10  for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number of processors  13  may be used, and such processors  13  may be present in a single device or distributed among any number of devices. In one aspect, a single processor  13  handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may be provided. In various aspects, different types of features or functionalities may be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below). 
     Regardless of network device configuration, the system of an aspect may employ one or more memories or memory modules (such as, for example, remote memory block  16  and local memory  11 ) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any combinations of the above). Program instructions may control execution of or comprise an operating system and/or one or more applications, for example. Memory  16  or memories  11 ,  16  may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein. 
     Because such information and program instructions may be employed to implement one or More systems or methods described herein, at least some network device aspects may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein. Examples of such nontransitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and “hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device. (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like. It should be appreciated that such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), “hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably. Examples of program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVA™ compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language). 
     In some aspects, systems may be implemented on a standalone computing system. Referring to  FIG. 34 , there is shown a block diagram depicting a typical exemplary architecture of one or more aspects or components thereof on a standalone computing system. Computing device  20  includes processors  21  that may run software that carry out one or more functions or applications of aspects, such as for example a client application  24 . Processors  21  may carry out computing instructions under control of an operating system  22  such as, for example, a version of MICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operating systems, some variety of the Linux operating system, ANDROID™ operating system, or the like. In many cases, one or more shared services  23  may be operable in system  20 , and may be useful for providing common services to client applications  24 . Services  23  may for example be WINDOWS™ services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system  21 . Input devices  28  may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof. Output devices  27  may be of any type suitable for providing output to one or more users, whether remote or local to system  20 , and may include for example one or more screens for visual output, speakers, printers, or any combination thereof. Memory  25  may be random-access memory having any structure and architecture known in the art, for use by processors  21 , for example to run software. Storage devices  26  may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form. (such as those described above, referring to  FIG. 33 ). Examples of storage devices  26  include flash memory, magnetic hard drive, CD-ROM, and/or the like. 
     In some aspects, systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to  FIG. 35 , there is shown a block diagram depicting an exemplary architecture  30  for implementing at least a portion of a system according to one aspect on a distributed computing network. According to the aspect, any number of clients  33  may be provided. Each client  33  may run software for implementing client-side portions of a system; clients may comprise a system  20  such as that illustrated in  FIG. 34 . In addition, any number of servers  32  may be provided for handling requests received from one or more clients  33 . Clients  33  and servers  32  may communicate with one another via one or more electronic networks  31 , which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other). Networks  31  may be implemented using any known network protocols, including for example wired and/or wireless protocols. 
     In addition, some aspects, servers  32  may call external services  37  when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services  37  may take place, for example, via one or more networks  31 . In various aspects, external services  37  may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications  24  are implemented on a smartphone or other electronic device, client applications  24  may obtain information stored in a server system  32  in the cloud or on an external service  37  deployed on one or more of a particular enterprise&#39;s or user&#39;s premises. In addition to local storage on servers  32 , remote storage  38  may be accessible through the network(s)  31 . 
     In some aspects, clients  33  or servers  32  (or both) may make use of one or snore specialized services or appliances that may be deployed locally or remotely across one or more networks  31 . For example, one or more databases  34  in either local or remote storage  38  limy be used or referred to by one or more aspects. It should be understood by one having ordinary skill in the art that databases in storage  34  may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means. For example, in various aspects one or more databases in storage  34  may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and so forth). In some aspects, variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill in the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular aspect described herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system. Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database” by those having ordinary skill in the art. 
     Similarly, some aspects may make use of one or more security systems  36  and configuration systems  35 . Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with aspects without limitation, unless a specific security  36  or configuration system  35  or approach is specifically require by the description of any specific aspect. 
       FIG. 36  shows an exemplary overview of a computer system  40  as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system  40  without departing from the broader scope of the system and method disclosed herein. Central processor unit (CPU)  41  is connected to bus  42 , to which bus is also connected memory  43 , nonvolatile memory  44 , display  47 , input/output (I/O) unit  48 , and network interface card (NIC) 53 . I/O unit  48  may, typically, be connected to peripherals such as a keyboard  49 , pointing device  50 , hard disk  52 , real-time clock  51 , a camera  57 , and other peripheral devices. NIC  53  connects to network  54 , which may be the Internet or a local network, which local network may or may not have connections to the Internet. The system may be connected to other computing devices through the network via a router  55 , wireless local area network  56 , or any other network connection. Also shown as part of system  40  is power supply unit  45  connected, in this example, to a main alternating current (AC) supply  46 . Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein. It should be appreciated that some or all components illustrated may be combined, such as in various integrated applications, for example Qualcomm or Samsung system-on-a-chip (SOC) devices, or whenever it may be appropriate to combine multiple capabilities or functions into a single hardware device (for instance, in mobile devices such as smartphones, video game consoles, in-vehicle computer systems such as navigation or multimedia systems in automobiles, or other integrated hardware devices). 
     In various aspects, functionality for implementing systems or methods of various aspects may be distributed among any number of client and/or server components. For example, various software modules may be implemented for performing various functions in connection with the system of any particular aspect, and such modules may be variously implemented to run on server and/or client components. 
     The skilled person will be aware of a range of possible modifications of the various aspects described above. Accordingly, the present invention is defined by the claims and their equivalents.