A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems.
Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data.
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