The number and lethality of violent extremist plots motivated by the Salafi-jihadist ideology have been growing for nearly the last decade in many parts of the world including both the United States and Western Europe. Although detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, it remains a significant challenge to law enforcement due to the issues of both scale and dynamics. The current article frames its approach to monitoring the radicalization pattern of behaviors as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT (Investigative Search for Graph-Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. This article presents the overall INSiGHT architecture and is aimed at assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence and allow for the better prioritization of limited investigative resources. The authors demonstrated the performance of INSiGHT on a variety of datasets, to include small synthetic radicalization-specific datasets and a real behavioral dataset of time-stamped radicalization indicators of recent U.S. violent extremists. (publisher abstract modified)
INSiGHT: A System To Detect Violent Extremist Radicalization Trajectories in Dynamic Graphs
NCJ Number
254302
Journal
Data & Knowledge Engineering Volume: 118 Dated: November 2018 Pages: 52-70
Date Published
November 2018
Length
19 pages
Annotation
This article proposes the development of a radicalization trend detection system as a risk assessment assistance technology that relies on data mined from public data and government databases for individuals who exhibit risk indicators for extremist violence, which would enable law enforcement to monitor those individuals at the scope and scale that is lawful, and account for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically.
Abstract