Enhancing Network Security: A Comprehensive Analysis of Intrusion Detection Systems

Given the increasing complexity and progress of intrusion attacks, effective intrusion detection systems have become crucial to protecting networks. Machine learning methods have become a potential strategy for identifying and reducing such attacks. This paper has conducted a comprehensive analysis of intrusion detection using machine learning methodologies. The aim is to thoroughly examine the current state of research, identify the barriers, and highlight potential solutions in this field. The study begins by analyzing the importance of intrusion detection and the limitations of traditional rule-based systems. Afterward, it explores the underlying principles and concepts of machine learning and how they are practically applied in the field of intrusion detection. This paper provides a comprehensive analysis of different machine learning algorithms, such as decision trees, neural networks, support vector machines, and ensemble methods. The primary objective of this study is to assess the effectiveness and limitations of employing these techniques for identifying various forms of intrusions. Three algorithms are used to classify the NSL-KDD dataset, namely Cascade Backpropagation Neural Networks (CBPNN), Layered Recurrent Neural Networks (LRNN), and Forward-Backward Propagation Neural Networks (FBPNN). Results have shown that CBPNN outperformed by achieving 95% accuracy.