Machine Learning in Digital Forensic Analysis
Angel Justo Jones · Advances in digital crime, forensics, and cyber terrorism book series · 2024
Machine learning (ML) is transforming digital forensic analysis by enhancing the speed, accuracy, and depth of evidence examination and interpretation. This chapter explores the integration of ML algorithms into digital forensic workflows, including anomaly detection, pattern recognition, and predictive modeling. It discusses how various machine learning techniques, such as supervised and unsupervised learning, deep learning, and reinforcement learning, are applied to tasks like data classification, incident response, and evidence triage. Challenges such as model interpretability, data privacy, and adversarial attacks are addressed alongside emerging solutions to improve robustness and reliability. Through case studies and practical applications, this chapter underscores the impact of machine learning on evolving forensic capabilities, contributing to more efficient investigations and enhanced decision-making processes.