A Comprehensive Analysis of Machine Learning Algorithms in Intrusion Detection Systems

Abstract

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding computer networks from unauthorized access and malicious activities. With the increasing complexity and diversity of cyber threats, the demand for effective IDS solutions has surged, leading to the exploration of various machine learning algorithms for intrusion detection. This paper presents a comprehensive analysis of machine learning algorithms in IDS, aiming to evaluate their performance, strengths, and limitations across different datasets and scenarios. The analysis encompasses a wide range of machine learning techniques, including supervised, unsupervised, and semi-supervised algorithms. We systematically review the literature and categorize the algorithms based on their approach, such as anomaly detection, signature-based detection, and hybrid methods. Each category is evaluated in terms of detection accuracy, false positive rate, scalability, and computational efficiency. Our findings reveal that while traditional machine learning algorithms, such as Support Vector Machines (SVM) and Decision Trees, offer robust performance in specific contexts, they often struggle with adaptability to evolving threats and scalability issues. In contrast, deep learning algorithms, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), demonstrate superior performance in capturing complex patterns and anomalies in network traffic data. However, their computational demands and interpretability remain significant challenges. Moreover, we investigate the impact of dataset characteristics, such as class imbalance, feature dimensionality, and data distribution, on the performance of machine learning algorithms. We highlight the importance of dataset preprocessing techniques, feature selection methods, and model optimization strategies in improving IDS effectiveness.  In conclusion, this paper provides valuable insights into the strengths and limitations of machine learning algorithms in IDS. By understanding the capabilities and trade-offs of different approaches, cybersecurity practitioners can make informed decisions in selecting and deploying IDS solutions tailored to their specific needs and requirements. Future research directions, including the integration of ensemble learning techniques and the development of explainable AI methods, are also discussed to advance the field of intrusion detection and enhance network security in the face of evolving cyber threats.