Machine Learning for Threat Detection
L. A. Anto Gracious, Siva Subramanian R · Advances in computational intelligence and robotics book series · 2025
Anomaly detection identifies patterns that deviate from normal behavior, signaling threats in cybersecurity, finance, healthcare, and industry. Traditional threat detection struggles with data volume and complexity, making ML a key solution for modeling large datasets. This chapter outlines ML techniques for anomaly detection, focusing on threat analysis. It reviews supervised, unsupervised, semi-supervised learning, deep learning, and neural networks, highlighting pros, cons, and applications like fraud, intrusions, faults, and health anomalies. Key factors like data preprocessing, feature engineering, and model evaluation are discussed. Challenges include imbalanced data, real-time detection, and model interpretability. The chapter also explores combining methods for better accuracy, transfer learning, AI explainability, and real-time systems as future trends. This chapter explores a clear view of current ML use in anomaly detection and future directions.