Improved Robust Adversarial Model against Evasion Attacks on Intrusion Detection Systems

Abstract

This research develops improved Robust Adversarial Models (RAM) to enhance Intrusion Detection Systems’ (IDS) robustness against evasion attacks. Malicious packets crafted using Scapy were infused into open-source datasets NSL-KDD and CICIDS obtained from Kaggle. Experiments involved passing this traffic through baseline IDS model such as in a free open-source IDS Snort and the improved RAM. Training processes employed perturbations using Generative Adversarial Networks (GAN), Fast Gradient Sign Methods (FGSM), and Projected Gradient Descent (PGD) against reinforcement learning of features and labels from the autoencoder model. The robust adversarial model showed 34.52% higher accuracy, 59.06% higher F1-score and 85.26% higher recall than the baseline IDS Snort model across datasets. Comparative analysis demonstrated the improved RAM’s enhanced resilience, performance, and reliability in real-world scenarios, advancing IDS models' and network infrastructures' security posture.