AI-Enhanced Cybercrime Profiling Understanding Behaviorial Patterns in Online Offenders

Abstract

The evolution of cybercrime poses significant threats to global security, with perpetrators exploiting digital vulnerabilities using increasingly sophisticated techniques. Profiling such offenders demands innovative approaches beyond traditional methods. This paper explores the integration of Artificial Intelligence (AI) in enhancing cybercrime profiling by identifying behavioral patterns among online offenders. Leveraging Natural Language Processing (NLP), Machine Learning (ML), and neural networks, AI systems can detect suspicious activities, predict potential threats, and analyze digital footprints to classify offenders into behavioral archetypes. The study discusses various AI models used for behavioral profiling, such as decision trees, clustering algorithms, and recurrent neural networks (RNNs), and evaluates their accuracy and limitations. Through a systematic literature review and case study analysis, this research highlights the role of AI in early detection, real-time monitoring, and predictive profiling. It also outlines the ethical considerations and challenges in adopting AI-driven profiling systems in law enforcement. The findings support the conclusion that AI-enhanced behavioral profiling is a promising tool for modern cybersecurity and forensic intelligence.