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                            Funding & Awards
 Dynamic, Graph-Based Risk Assessments for the Detection of Violent Extremist Radicalization Trajectories Using Large Scale Social and Behavioral Data
Award Information
Awardee
              COLORADO STATE UNIVERSITY
Award #
              2017-ZA-CX-0002
Funding Category
              Competitive Discretionary
Location
                    FORT COLLINS, CO
Congressional District
              2
Status
              Closed
Funding First Awarded
              2017
Total funding (to date)
              $731,194
Original Solicitation
              NIJ FY17 Research and Evaluation on Domestic Radicalization to Violent Extremism
Description of original award (Fiscal Year 2017, $731,194)
This project aims to produce an empirically-tested dynamic risk assessment protocol to anticipate the imminent risk of violence, and a computationally efficient tool based on this new protocol that enables law enforcement to mine, monitor, and screen for the occurrence of risk indicators in large law enforcement databases.  The application builds on and expands a previously NIJ-funded database of overt behavioral indicators associated with jihadist inspired terrorists.
The proposed study will enhance the existing database by adding a wider range of overt behavioral indicators and new cases since 2015.  Machine learning will be applied to the enhanced database of behavioral indicators to develop algorithms for the dynamic risk assessment tool.  This tool will be designed to integrate with existing law enforcement databases and sources to monitor and screen for in near real-time those individuals who pose significant risk for violent terrorism. This approach is considered machine learning because the probabilities and patterns are learned and refined using baseline data sets from validated terrorism cases. This set of learned probabilities/patterns is then used to on new law enforcement data sets to produce probability scores that indicate a potential violent terrorist.
This project contains a research and/or development component, as defined in applicable law.
CA/NCF



  
Grant-Funded Publications
        
                      Adversarial Autoencoder Data Synthesis for Enhancing Machine Learning-Based Phishing Detection Algorithms
                  Enhancing Investigative Pattern Detection via Inexact Matching and Graph Databases
                  VideoTrain plus plus: GAN-based adaptive framework for synthetic video traffic generation
                  Dynamic, Graph-Based Risk Assessments for the Detection of Violent Extremist Radicalization Trajectories Using Large Scale Social and Behavioral Data
                  Finding Emergent Patterns of Behaviors in Dynamic Heterogeneous Social Networks
                  INSiGHT: A System To Detect Violent Extremist Radicalization Trajectories in Dynamic Graphs

  
Date Created: September 29, 2017
    
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