Machine Learning Algorithms for Threat Detection and Evidence Analysis
Luay Albtosh, Ryan Yates · Advances in computational intelligence and robotics book series · 2025
The integration of machine learning (ML) into digital forensics and cybercrime investigation has revolutionized the way threats are detected and evidence is analyzed. This chapter explores the role of ML algorithms in enhancing the efficiency and accuracy of threat detection and forensic analysis. It presents a comprehensive overview of various supervised, unsupervised, and reinforcement learning techniques applied to anomaly detection, malware classification, intrusion detection systems (IDS), and digital evidence triage. Emphasis is placed on real-time detection capabilities, scalability, and adaptability of ML models in dynamic cyber environments. The chapter also addresses challenges such as data imbalance, adversarial attacks, explainability, and ethical considerations related to privacy and accountability. Through illustrative case studies and comparative analysis, this chapter provides practical insights into the current state of ML in digital forensics and highlights promising directions for future research and implementation.