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The increased participation in digital networks for communication, commerce, and critical infrastructure, calls for a robust Network Intrusion Detection System. This paper systematically examines the current landscape of NIDS, by analyzing the methodologies, algorithms, and technologies used in finding different forms of network threats. The paper begins by presenting the fundamental principle of the Intrusion Detection System and the evolving threat scenario and the benefits of using ML and Deep Learning when employed in this use case. We also dive into the categorization of NIDS into Signature-Based, anomaly-based, and Hybrid approaches. Evaluating each category's strengths and weaknesses. The paper aims to provide a comprehensive discussion and comparison of the various techniques proposed like MCF-MVO-ANN, an Intrusion detection system based on scalable K-means and Random Forest, etc. By synthesizing current knowledge this paper aims to serve as a valuable resource for researchers, practitioners, and decision-makers in the field of cybersecurity.
Lonare et al. (Fri,) studied this question.