Adversarial Attack Detection Approach for Intrusion Detection Systems
IEEE Access, cilt.12, ss.195996-196009, 2024 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 12
- Basım Tarihi: 2024
- Doi Numarası: 10.1109/access.2024.3520406
- Dergi Adı: IEEE Access
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
- Sayfa Sayıları: ss.195996-196009
- Anahtar Kelimeler: Adversarial attacks, deep learning, intrusion detection systems, machine learning resilience, reconstruction error, security
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
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.