Machine Learning for Intrusion Detection

This chapter explores the use of machine learning as a crucial development in the advancement of intrusion detection systems (IDSs). Conventional signature-based methods, though successful against recognized threats, are becoming less sufficient in identifying zero-day exploits and adaptive adversary actions. Machine learning overcomes these restrictions by allowing models to learn from data trends, adjust to changing attack methods, and diminish dependence on fixed rule sets. The chapter examines supervised techniques like decision trees, support vector machines, and ensemble methods, which offer excellent interpretability and reliable performance in structured settings. Unsupervised and semi-supervised techniques are noted for detecting new anomalies without requiring large amounts of labeled data, while deep learning architectures—especially convolutional, recurrent, and attention-oriented networks—show exceptional proficiency in examining complex, high-dimensional traffic patterns. Significant challenges are recognized as well, such as class imbalance, adversarial interference, feature drift, and the necessity for interpretable decision-making. Instead of portraying machine learning as a one-size-fits-all answer, the chapter highlights its function as a core facilitator for adaptive, intelligence-based IDSs, stressing the importance of combining technological advancements with human skills and management supervision.