Comparative Evaluation of Machine Learning Algorithms for Intrusion Detection

Abstract

This study undertakes a comparative examination of machine learning algorithms used for intrusion detection, addressing the escalating challenge of safeguarding networks from malicious attacks in an era marked by a proliferation of network-related applications. Given the limitations of conventional security tools in combatting intrusions effectively, the adoption of machine learning emerges as a promising avenue for bolstering detection capabilities. The research evaluates the efficacy of three distinct machine learning algorithms—Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Naive Bayes—in identifying diverse attack categories, including Denial of Service, Probe, Remote to Local, and User to Root.
 Conducted on the NSL-KDD dataset, the analysis unveils CNN and RNN as superior performers compared to Naive Bayes, particularly in terms of detection accuracy. These findings extend value to both researchers and practitioners in the realm of intrusion detection systems, offering insights into optimal algorithmic choices. Furthermore, the study's implications resonate within broader contexts, such as the advancement of secure automation in industrial environments and the realm of automobile automation. Overall, this research contributes to the ongoing efforts to fortify network security and promote the development of safer technological landscapes.

An intrusion detection system based on convolution neural network

“…The model reported by Jiang et al (2020) lacks a parameter tuning function, leading to an over-reliance on parameter selection and subsequently yielding a low precision rate. In addition, the study by Oluwakemi, Muhammad & Anyachebelu (2023) has undertaken more comprehensive efforts. They evaluate the efficacy of three distinct machine learning algorithms—CNN, recurrent neural networks (RNN), and Naive Bayes—in identifying diverse attack categories.…”