Adversarial attack and defense in AI-powered intrusion detection - Nguyen, Tuyen T.; Tran, Uyen H.; Nguyen, Hoa N. (Journal of Computer Science and Cybernetics, 2025)
The increasing sophistication of cyberattacks, causing global damages estimated at $9.22 trillion in 2024, highlights the critical importance of robust Intrusion Detection Systems (IDS). AI-driven IDS frameworks, such as APELID, demonstrate impressive detection accuracy leveraging novel machine learning. However, these systems remain vulnerable to adversarial machine learning (AML) attacks, which craft deceptive inputs to bypass detection mechanisms. In this paper, we propose APELID+, an enhanced IDS framework integrating adversarial training and feature squeezing techniques to effectively counter AML threats. We systematically evaluate APELID’s vulnerabilities using comprehensive adversarial attack strategies, including both white-box (FGSM, JSMA, PGD, DeepFool, CW) and black-box attacks (ZOO, HSJA). Experimental results on the CSE-CIC-IDS2018 dataset reveal a significant reduction in APELID’s accuracy (from 99.7% to as low as 1.14% under FGSM attacks). The enhanced APELID+ achieves robust performance, maintaining 98.73% accuracy under combined adversarial conditions, surpassing state-of-the-art methods such as Apollon and RAIDS.