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INSiGHT: A system to detect violent extremist radicalization trajectories in dynamic graphs

Benjamin W. K. Hung,A. Jayasumana,Vidarshana W. Bandara

Published 2018 in Data & Knowledge Engineering

ABSTRACT

Abstract 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 U.S and Western Europe. While 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. We propose 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, and enables law enforcement to monitor those individuals at the scope and scale that is lawful, and accounts for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically. We frame our approach to monitoring the radicalization pattern of behaviors as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT ( In vestigative S earch for G rap h - T rajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. This paper 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. We 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.

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