Adversarial Attack Detection Approach for Intrusion Detection Systems
Elif Değirmenci, İlker Özçelik, Ahmet Taylan Yazıcı · IEEE Access · 2024
The adoption of deep learning has exposed significant vulnerabilities, especially to adversarial attacks that cause misclassifications through subtle small perturbations. Such attacks challenge security-critical applications. This study addresses these vulnerabilities by proposing a novel adversarial attack detection method leveraging data reconstruction errors. We evaluate this approach against three well-known adversarial attacks—Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Basic Iterative Method (BIM)—on Intrusion Detection Systems. Our method combines reconstruction error alongside aleatoric, epistemic, and entropy metrics to distinguish between original and adversarial samples. Experimental results show that our approach achieves a detection success rate of 92% to 100%, outperforming existing methods, particularly at low perturbation levels. This research enhances the robustness and reliability of machine learning models against adversarial threats by using effective error metrics in adversarial detection.