Machine learning (ML) techniques have significantly enhanced decision support systems to render the systems more accurate, efficient, and faster. ML classifiers in securing networks, on the other hand, face a disproportionate risk of mettle adversarial attacks as compared to other areas, including spam filtering and intrusion, and virus detections, and this introduces a continuous competition between malicious users and preventers. Attackers test ML models with inputs that have been specifically produced for evading these models and provide an inaccurate forecast. This paper presents a comprehensive review of attack and defensive techniques in ML-based NIDSs. It highlights the current serious challenges that the systems face in preserving robustness against adversarial attacks. Based on our analysis, with respect to their current superior performance and robustness, ML-based NIDS require rapid attention to develop more robust techniques to withstand such attacks. Finally, we discuss the current existing approaches in generating adversarial attacks and reveal the limitations of current defensive approaches. In this paper, the most recent advancements, such as hybrid defensive techniques that integrate multiple approaches to prevent adversarial attacks in NIDS, have effectively highlighted the ongoing challenges.
Alasad et al. (2025) studied this question.