Analyzing the Impact of Machine Learning Techniques for Intrusion Detection Systems: A Review

This review paper aims to assess how Machine Learning (ML) approaches affect Intrusion Detection Systems (IDS), a vital cybersecurity component. Traditional IDS need to be improved due to the increasing complexity and frequency of cyber-attacks, which are made worse by the widespread use of Internet of Things (IoT) devices. The purpose of this paper is to examine how sophisticated machine learning algorithms can enhance the overall efficacy, efficiency, and accuracy of IDS in identifying and countering these dynamic threats. The research methodology involved a comprehensive review of studies conducted from 2014 to 2024, focusing on various ML algorithms applied to different datasets used in IDS, such as KDD Cup ’99, NSL-KDD, and CICIDS2017. The paper systematically categorizes these studies by the machine learning techniques employed, the datasets utilized, and the performance metrics such as accuracy, precision, and recall. The main findings show that ML techniques have considerably improved IDS performance, especially ensemble learning and hybrid classifiers. Like, the use of Random Forests and Deep Neural Networks has improved detection, accuracy, and decreased false positives. However, there are still issues to be resolved, like controlling high false positive rates, requiring updated datasets, and enhancing feature selection methods. The research conclusion suggests that although ML has significantly improved IDS capabilities but more efforts are still required to maximize these systems for practical use. Future research should focus on creating more reliable datasets, improving feature selection techniques, and exploring novel algorithms that can adapt to the continuously evolving landscape of cyber-threats.