An Adaptive Defense Framework for Enhancing Adversarial Robustness in Deep Learning-based Network Intrusion Detection Systems

Abstract

This study investigated the robustness of deep learning-based Network Intrusion Detection Systems (NIDS) against adversarial attacks by proposing a confidence-aware adaptive defense framework. The proposed approach integrates a baseline feedforward neural network, an adversarially trained robust model, and an adversarial detector to dynamically select the most appropriate prediction path based on detector confidence. Experimental evaluation under single-step, multi-step, and adaptive adversarial attack scenarios demonstrated that the framework significantly improves detection robustness while maintaining high classification accuracy on clean network traffic. The adaptive fusion strategy effectively mitigates the impact of adversarial perturbations, reducing misclassification rates and enhancing the reliability of intrusion detection in dynamic cybersecurity environments. These findings confirm that confidence-guided adaptive defense mechanisms provide a practical solution for strengthening the resilience of AI-driven NIDS against evolving attack strategies. However, the proposed framework was evaluated using controlled experimental settings and specific attack models, which may not fully represent the diversity of real-world cyber threats. Future work will focus on validating the framework in large-scale operational networks, extending it to advanced zero-day and adaptive attacks, and investigating lightweight deployment strategies for real-time edge and cloud-based cybersecurity applications.