Proceedings of International Conference on Applied Innovation in IT  ·  2026/06/12  ·  Vol. 14  ·  Issue 4  ·  pp. 21–31
AI-Based Mitigation Strategies for Evasion Attacks in Intrusion Detection Systems
Intisar Adnan Hasan and Abdulbasit Al Azzawi
📄 Download PDF DOI: Under indexing
Abstract
The high rate of artificial intelligence (AI) has had a major impact on Enhancing the intrusion detection systems (IDS). However, these systems remain highly vulnerable to adversarial evasion attacks, in which adversarial inputs are intentionally designed to avoid detection systems. This research paper is a systematic review of recent studies on evasion attacks on intrusion detection systems, and aiming to identify to the popular attack methodology, most widely datasets, and up-to-date defences. The results demonstrate that gradient-based attacks, in particular Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), are the most widely used methods of testing system robustness. In addition, it can be seen that there is a strong reliance on standard benchmark datasets, while real-world evaluation scenarios are minimal. Despite the wide range of defences mechanisms, most of them have a hard time keeping pace with adaptive and advanced adversarial threat. This review points out the gaps in current studies that are critical such as a lack of a variety of evaluation conditions and more adaptive and robust detection strategies. The research provides on the existing research issues and outlines key trends to pursue by future research, which could help create more robust and resilient cybersecurity.
Keywords
Evasion Attacks Robustness Adversarial Machine Learning Advanced AI Mitigation
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