Anti-Crime Potential of Machine Learning: Predictive Analytics for Preventing Digital Terrorism Activities

Abstract

Advances in digital technology - particularly Web3’s pseudonymity and decentralized naming systems, combined with information flows’ anonymity, accessibility, and cross-border nature - enable terrorist organizations to recruit members and perpetrate discrete socially dangerous acts. Conventional reactive counterterrorism measures prove inadequate against rapid illicit content dissemination that leaves detectable digital traces. This study explores artificial intelligence’s (AI) counter-criminal potential on machine learning and predictive analytics for proactively identifying and preventing terrorist activity through behavioral indicators and digital footprints that facilitate a strategic shift to proactive security paradigms. The research develops a multimodal analytical framework integrating natural language processing, computer vision, audio analysis, and social network analysis, detailing the complete machine learning pipeline from data preprocessing to model deployment. It examines the “RED-Alert” system as practical implementation and proposes a novel “Threshold Adaptive Intervention” (PORA) module utilizing graph neural networks and time-series analysis for digital risk assessment. Machine learning excels at threat detection and digital evidence generating, necessitating reevaluation of internet service providers’ (ISP) liability - particularly collective digital inaction. A differentiated liability framework accounts for providers’ technical influence while treating AI-derived risk indicators as ancillary tools for establishing individual culpability. Machine learning and predictive analytics enable a strategic shift to proactive counterterrorism.