Adversarial Machine Learning in Intrusion Detection Systems

This chapter explores the vulnerabilities of AI-driven Intrusion Detection Systems (IDS) to Adversarial Machine Learning (AML) attacks and presents a multi-layered defense framework. It examines how adversarial tactics such as evasion, poisoning, and model inversion mislead IDS models using crafted inputs. Techniques like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Generative Adversarial Networks (GANs) are used to bypass detection. To counter these threats, the chapter proposes adversarial training, input preprocessing, gradient masking, ensemble learning, and Explainable AI (SHAP, LIME). Real-world case studies in banking and cloud environments highlight improvements in detection accuracy and reduction in false negatives. Future research directions include adaptive learning, privacy-preserving federated systems, and real-time explainability. The proposed approach enhances IDS robustness by up to 50%, offering practical solutions to secure enterprise systems against evolving adversarial threats.

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