Artificial Intelligence in Digital Forensics: A Review of Cyber-Attack Detection Models and Frameworks

Abstract

In the rapidly evolving landscape of cyber threats, traditional digital forensic methods often fall short in addressing the scale, speed, and complexity of modern attacks. This review explores the integration of Artificial Intelligence (AI) into digital forensics as a transformative approach for proactive cyber-attack detection and investigation. Drawing on over 30 peer-reviewed publications from 2016 to 2024, the study categorizes AI techniques into machine learning, symbolic AI, and hybrid systems, evaluating their applications in anomaly detection, forensic triage, behavioral profiling, and multimedia analysis. Comparative analysis highlights the strengths of AI—including automation, scalability, and predictive modeling—while also addressing key challenges such as explainability, adversarial robustness, and ethical concerns. Emphasis is placed on the need for explainable AI (XAI) frameworks, real-world validation, and legally admissible evidence generation. The review also examines notable AI-powered frameworks like D4I, Fronesis, and AIFIS, assessing their technical and legal efficacy. Finally, the paper outlines critical research gaps and future directions, including the development of open benchmarks, adversarially resilient models, and privacy-preserving forensic architectures. This synthesis aims to guide researchers and practitioners toward developing trustworthy, scalable, and legally sound AI-based digital forensic solutions.