A Review of Machine Learning Techniques for Network Intrusion Detection Systems

Abstract

Security researchers rely heavily on Network Intrusion Detection Systems (NIDS) to keep an eye on network traffic and notify administrators of any suspicious activities. The purpose of this paper is to offer a comprehensive overview of intrusion detection systems (IDS), including the following topics: fundamentals, kinds of IDS, methods for detecting intrusions in NIDS, the architecture of IDS, data pre-processing, and examples of ML techniques used in NIDS. This covers several detection methods, including signature-based, anomaly-based, specification-based, and behavior-based approaches, as well as their advantages and disadvantages in recognizing both existing and new cyber threats. The review also covers the architecture of NIDS which consists of network sensors, preprocessors, network traffic analysis, alert generation and security analysis. A variety of ML techniques, including supervised, unsupervised, semi-supervised, ensemble, and deep learning (DL) approaches, are being explored to improve the accuracy and adaptability of intrusion detection systems (IDS). Other applications such as DoS/DDoS attack detection, Malware detection, Botnets, Brute force attacks, Insider compromise, IoT compromise and Critical infrastructure threats are also shown. Despite all the challenges in terms of false positives, scalability, computational complexity, data quality, and novel attack styles, the features that ML can provide for intelligent, adaptive, and accurate intrusion detection systems are appealing.