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Identifying and Analyzing Patterns of Evasion HM0210-13-1-0005 Investigator: Shashi Shekhar (U Minnesota) Collaborators: Renee Laubscher, James Kang Kickoff Date: September 2013 Identifying and Analyzing Patterns of Evasion University of Minnesota PI: Shashi Shekhar Year of Grant: 2013 Technical Challenge • Develop space-time aware methods to model evasive behavior by insurgents and other security targets. • The behavior of security targets can be exploited to identify and provide intelligence about locations and schedules of target. Research Approach: We propose a method to distinguish between evasive and non-evasive behaving targets by quantifying the space-time entropy (predictability) of individuals’ movement. Also we propose a complementary algorithm which identifies “blackholes,” areas where no target movement is observed, despite predictions that such movement would occur. A list of targets found to have patterns of evasion are generated. Analyzing these evasion patterns will expose potential sighting and interception places and time-slots of targets on the list. Accomplishments/Results Transition Opportunities •Extend capabilities of Oracle Spatial to facilitate a complete workflow dataset from dataset input to actionable information. Collaborations and Teaming • Develop a conceptual model to represent the spatio-temporal predictability of targets’ movement across a space. • Create a conceptual model defining classes of blackhole events which will help estimate the expected continuity of observation through denied areas. • Develop a conceptual model for hypothesized movement generation based on trajectory fragments. • Develop a conceptual model for Denial and Deception Aware Return Periods. • Mark Abrams, a consultant for the National Reconnaissance Office with experience in GPS deprived areas. • Prof. May Yuan at the University of Oklahoma working on movement pattern analysis • BI Incorporated, he makers of ankle-bracket GPS tracking devices for felons out on parole, to utilize the anonymized GPS-track datasets of offenders for research purposes. Potential Research Payoff PI: Shashi Shekhar [email protected] 612-624-8307 Describe what impact successful research will deliver. Example: • Creation of models for identifying evasive patterns. • Success of predicting target movements and schedules. • Building of tools embodying the new results. Future Efforts • Explore the tools and test bed to handle new blackhole patterns, more advanced movement hypothesis and temporally correlated return periods. Contact Info TM: James M. Kang [email protected] Identifying and Analyzing Patterns of Evasion • Identify the behavior of Insurgents and Security Targets by using location data. • By identifying these behaviors, actionable intelligence about locations and schedules of target individuals can be provided. Objectives • Develop space-time aware methods for modeling patterns of evasive behavior by insurgents and security targets: • Distinguish between evasive and non-evasive behaving targets by quantifying space-time entropy of individual’s movement. • Identify Blackhole Regions where no target movement is observed despite predictions. • Generate hypothesis about target location and travel routes by using trajectory fragments. • Model Denial and Deception Aware Return Periods to estimate a target’s schedule to aid in interception and surveillance. Anticipated Results • Mathematical Models • Develop a graph based spatio-temporal data model to represent return periods. • Computer Algorithms • Design new scalable data mining algorithms to find; • • • • Space-Time Entropy Blackholes Theory-Based Movement Predictions Return Periods • Data Analysis Methods • Develop new interest measures to quantify the proposed patterns of life. • Analytical Tools • Develop new software and extend capabilities of existing software to facilitate the workflow from dataset to actionable information. Applicability • “Predictability kills” • In Operation Areas, soldiers are advised to avoid geo-tagging, which can reveal their location to adversaries. • This advice is kept in mind by the terrorists as well. • In operation areas like Afghanistan, non-evasive tribal groups operate (e.g., villagers, nomads, traders) with different patterns-oflife, or different movement patterns, than evasive terrorists. Approach • Two Phases: • Identify Evasion Patterns • Distinguish between evasive and non-evasive behaving targets by quantifying the space-time entropy • Identify blackholes, where no target movement observed despite predictions • Analyze Evasion Patterns • Movement hypothesis for predicting movement in denied areas • Model return periods to quantify and identify anomalous visits in target movement data. • By identifying these evasive behaviors, actionable intelligence about locations and schedules of target individuals can be provided. Science 1-4 • Space-Time Entropy Discrimination • Identify target groups based on historical movement patterns by using entropy measures. • Shannon’s Diversity Index (SDI) • Commonly used measure of entropy. • Quantify predictability. • SDI does not distinguish between clustered and unclustered distributions(SDI may return the same value even though the data is more concentrated). • SDI is not independent of space partitioning (Depending on the partition size, SDI score may change even underlying data does not). • Develop a conceptual model (which addresses the limitations of SDI) to represent the spatio-temporal predictability of target’s movement across space. Science • Blackhole and Patterns of Evasion Detection • Blackhole: Areas where no target movement is observed although target movements are predicted • Identify significant mismatches between observations (expected vs. observed). • Differentiate population and target evasion. • Extrapolate Potential Path 2/4 Science 3/4 • Theory Based Movement Hypothesis Generation • Traditional Data Mining Algorithms can not be used inside blackholes due to lack of observations. • Predict behavior inside blackhole. • Use transportation networks • Points of interest • Hypothesize routes and locations based on the recorded observations. • We know starting and ending points • We know transportation networks • We predict the most likely routes, main corridors and key locations that may be used. Science 4/4 • Return Period in Movement Datasets Once in 30 days • Model routine activities of people Routine Activities • Easier data mining on movement datasets • Easier highlighting of anomalous activities • Develop a conceptual model for denial-anddeception aware return periods to quantify and identify anomalous places and visits. • Challenges; • Space and Time Partitioning • Statistical Identification Measures for Anomalous Places and Visits • An efficient algorithm to deal with spatial big data. Conclusion • Identifying the behavior of terrorists can exploit their intentions. • Insurgents and Security Targets use Denial, Deception and Evasion Techniques to mask their movement. • These techniques may cause wrong operational and tactical decisions. • By identifying these evasive behaviors actionable intelligence about locations and schedules of target individuals can be provided.