Understanding the Human Network

1. Understanding the Human Network Martin Kruger martin.kruger1@navy.mil LCDR Jodie Gooby Johanna.gooby@navy.mil November 2008

2. Program Goal • Develop and then translate models of “who, where, how, when, why” to suggestion of action

3. Areas of Emphasis (1/2) • Advanced Sensors – development of high information content sensors that can detect, classify, identify and/or geolocate actors or behaviors of interest at the sensor. • Biometrics – development of capabilities that enable person identity dominance 24/7 over large areas. • Tagging, Tracking and Location (TTL) – development of capabilities enabling the persistent tracking of actors or objects of interest and the classification of collected track data. • Urban Situational Awareness – development of sensors to enable through structure situational awareness and the development of capabilities enabling structure classification. • Wide Area Surveillance – the development of a tier-2 wide area surveillance payload, with supporting communications and the development of processing services that can translate persistent surveillance to actionable intelligence • Sensor Planning and Management – development of decision aids that help the warfighter understand what to look for, where to look and how to look for it.

4. Areas of Emphasis (2/2) • Determining Intent – development of software applications that expose the intent of irregular actors in clutter (who/where). • Expose Enemy Structure & Decision Modeling – development of capabilities that can automatically expose enemy modus operandi with respect to their network structure or decision processes (how, why, when). • Cultural Intelligence – development of the capabilities required to make cultural intelligence tactically actionable. • Cognitive Information Operations– development of warfighter decision aids based on high resolution models, which enable decision space shaping or exploitation (proactive action). • ISR to C2 – development of capabilities related to the complete integration of ISR into current tactical operations.

5. The Domain of Irregular Warfare • Analysis approaches utilized against conventional forces have in large part been transferred to the domain of security and stabilization with limited success. • Case management (cannot be scalable) • Static • What we believe we need to do better: • Expose at risk subpopulations • Efficiently and effectively act • Disrupt • Influence • Stimulate

6. Capability Needs • Develop persona and network signatures • The “signal” needed to support classification • Map signatures to behaviors • Relevance of the signature • Develop persona and network models that are causal to outcomes of interest • Actionable intelligence • Analyze personas and networks as tracks • Time as an independent variable • Understand how to classify subject tracks • Likelihood ratios (suspicion, clutter)

7. Persona, Human Network Fingerprints Persona (Persona discovery involves clustering entities by their attributes) Static Signature Persona = f (network stability, communication patterns, interactions with other humans and networks, proximity to values, proximity to goals, proximity to themes, proximity to places, material or events, observed behaviors/actions) or metadata Dynamic Signature Persona (Xn, t+x) = Persona (Xn, t) * IO(n) Network Static Signature Human Network = f (membership, persona composition, structure, stability, communication patterns, interactions with other human networks, proximity to values, proximity to goals, proximity to themes, proximity to other entities or events, observed behaviors/actions) or metadata Dynamic Signature Human Network (Xn, t+x) = Human Network (Xn, t) * IO(n)

8. Communication Patterns • Research questions: • What are the set of terms that uniquely describe the identity of a persona or a network • What are the set of terms that describe the function of a persona or a network • Present state of the art: • Largely frequency based metrics • Required capability: • What collective set of measurements can be made that uniquely describe the network, allow for network decomposition to nodes, shed light on its function and provide a predictive capability? • Frequency, duration, track heading, likelihood of a match to a template of interest, likelihood of a match to a clutter model, other

9. Key Technologies • Clustering in N-dimensional space • Similarity measures across N-dimensional space • Change detection • Causality • “Fusion” of soft sciences with computational techniques

10. Other Research Challenges • Poor data environments • Networks are undersampled • Reliance on second hand data • Source reliability • Persona, human network or decision model resolution • How good is good enough • Model definition • Normalization of terms • Mapping to behaviors • Human network models • Aggregation in N-dimensions • Decision models • Actionable via time as a independent variable • Closeness measures • Calculation of the closeness between N-dimensional, un-normalized vectors • Transfer learning • Moving models in space and time • Non linear response • Low input, big output

11. Individuals Trending Toward Persona Types Persona A Persona B #306 #3 #200 #397 #360 #300 11

12. Network Behavior Over Time 12

13. Behavioral Trajectories Enabling Action Existing Past Event Data ID threat locations Signature Analyst 1 3 HUMINT SIGINT Incident Data IMINT MASINT Intel Feeds Detect Change in TTPs 4 2 Force Action Optimization Proprietary & Confidential 13

14. Uncertainty Reduction - Variable Reduction

15. Uncertainty Reduction with Cultural Models • Analysis Challenge: • How to track the movement of networks through time/space • Determine locale of the leadership cell, based on: • Persona signature • Infrastructure factors • Causally relevant cultural factors SPADAC Proprietary 15

16. Summary • Human network and persona signatures can be used to understand the human terrain and to model IO effects. • Higher resolution decision modeling enables own force action optimization