Investigative Pattern Detection Framework for Counterterrorism

TL;DR

This paper introduces INSPECT, a comprehensive framework combining machine learning and graph pattern matching to automate and enhance counterterrorism investigations by analyzing large-scale behavioral data.

Contribution

The paper presents a novel investigative pattern detection framework that integrates multiple computational tools for automated analysis and threat detection in counterterrorism efforts.

Findings

Validated on domestic jihadism dataset

Effectively identifies behavioral indicators and risk profiles

Automates large-scale forensic data analysis

Abstract

Law-enforcement investigations aimed at preventing attacks by violent extremists have become increasingly important for public safety. The problem is exacerbated by the massive data volumes that need to be scanned to identify complex behaviors of extremists and groups. Automated tools are required to extract information to respond queries from analysts, continually scan new information, integrate them with past events, and then alert about emerging threats. We address challenges in investigative pattern detection and develop an Investigative Pattern Detection Framework for Counterterrorism (INSPECT). The framework integrates numerous computing tools that include machine learning techniques to identify behavioral indicators and graph pattern matching techniques to detect risk profiles/groups. INSPECT also automates multiple tasks for large-scale mining of detailed forensic biographies, forming knowledge networks, and querying for behavioral indicators and radicalization trajectories. INSPECT targets human-in-the-loop mode of investigative search and has been validated and evaluated using an evolving dataset on domestic jihadism.