Investigative Pattern Detection Framework for Counterterrorism
Shashika R. Muramudalige, Benjamin W. K. Hung, Rosanne Libretti, Jytte, Klausen, Anura P. Jayasumana

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,…
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Taxonomy
TopicsTerrorism, Counterterrorism, and Political Violence · Cybercrime and Law Enforcement Studies · Data Quality and Management
