Application of Artificial Intelligence in Digital Crime Traceability Modern Methods and Evaluation of Their Effectiveness

Abstract

Artificial Intelligence (AI) is increasingly integral to digital forensics and cybercrime investigations, offering powerful tools to trace criminal activities across massive and diverse datasets. This article provides a comprehensive overview of modern AI-based methods for digital crime traceability – from machine learning algorithms and neural networks to natural language processing (NLP) and behavioral analytics – and evaluates their effectiveness in real-world use. Globally and regionally (with insights into Uzbekistan), law enforcement is embracing AI to automate evidence collection, analyze digital traces, and uncover patterns invisible to human analysts. In experimental studies, AI-driven frameworks have demonstrated high accuracy (over 94% in evidence classification) and substantial reductions in investigation time. Case studies illustrate how AI can swiftly sift network logs for anomalies, identify suspects via multimedia analysis, and reconstruct complex cybercrime timelines. We also examine challenges impeding implementation, including technical limitations (data scarcity, model interpretability, adversarial attacks), ethical concerns (bias, privacy), and legal hurdles (evidence admissibility). Through a formal review of current methods and metrics, as well as discussion of challenges and best practices, we highlight that AI is a transformative force in digital crime traceability.