Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects

Machine learning has brought significant advances in cybersecurity, particularly in the area of intrusion detection systems. This improvements can be mostly attributed to the ability of machine learning algorithms to identify complex relations between features in the data and to generalize well to unseen samples. Deep neural networks in particular contributed to this progress by enabling the analysis of large amounts of training data, significantly enhancing detection performance. However, machine learning models are vulnerable to adversarial attacks: manipulations of input data designed to mislead the models into making incorrect predictions. While much attention has been given to adversarial threats in unstructured data such as text and images, their effectiveness in structured data such as network traffic has not been as thoroughly explored. This survey seeks to fill this gap by providing an critical review of machine learning-based Network Intrusion Detection Systems (NIDS) and a thorough analysis of their vulnerability to adversarial attacks. We critically review existing NIDS research, highlighting key trends, strengths, and limitations, and we identify gaps in understanding that require further exploration. We further discuss emerging challenges and offer insights for developing more robust and resilient NIDS models. In summary, this paper aims to enhance understanding of adversarial attacks and defenses in NIDS and guide future research in improving the robustness of machine learning models in cybersecurity applications.