A Sensitivity-Driven Gradient Framework for Adversarial Sample Generation in Deep Learning-Based Network Intrusion Detection Systems

Abstract

Network intrusion detection systems based on deep learning have shown strong performance in modeling complex traffic patterns. However, recent studies indicate that these models can be vulnerable to carefully crafted adversarial perturbations, in which small modifications to traffic features lead to incorrect classification. This work proposes a sensitivity-driven adversarial generation framework (AGF) that identifies and perturbs the most influential traffic features that affect the classifier’s decision boundary. The framework integrates feature-level sensitivity estimation, stochastic sensitivity-guided feature partitioning, and threshold-guided optimization to generate statistically consistent adversarial samples with constrained perturbation magnitude. Experimental evaluation on CIC-IDS2017, CIC-IDS2018, and CIC-DDoS2019 achieves attack success rates of 94.82%, 93.56%, and 92.71%, respectively, while maintaining low perturbation distortion, KL divergence values below 0.013, and protocol violation rates close to 1–2%. A comparative analysis against FGSM, PGD, C&W, BotDefender, VMFCVD, and Boundary attacks demonstrates improved evasion capability with smaller, more structured perturbations.