A comprehensive review of machine learning's role in enhancing network security and threat detection
1 Independent Researcher, San Francisco, USA.
2 Independent Researcher, UK.
3 Independent Researcher, Seattle, Washington, USA.
4 Department of Cybersecurity, University of Nebraska, Omaha, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 877–886
Article DOI: 10.30574/wjarr.2024.21.2.0501
Publication history:
Received on 03 January 2024; revised on 11 February 2024; accepted on 13 February 2024
Abstract:
As network security threats continue to evolve in complexity and sophistication, there is a growing need for advanced solutions to enhance network security and threat detection capabilities. Machine learning (ML) has emerged as a powerful tool in this context, offering the potential to detect and mitigate threats in real-time by analyzing vast amounts of network data. This comprehensive review explores the role of machine learning in enhancing network security and threat detection. The review begins by providing an overview of the current landscape of network security threats and the challenges faced by traditional security approaches. It then delves into the fundamental principles of machine learning and its application to network security. Various machine learning techniques, including supervised learning, unsupervised learning, and deep learning, are discussed in detail, highlighting their strengths and limitations in the context of threat detection. Next, the review examines the application of machine learning in different aspects of network security, including intrusion detection, malware detection, anomaly detection, and behavioral analysis. Case studies and real-world examples are presented to illustrate the effectiveness of machine learning-based approaches in identifying and mitigating security threats. Furthermore, the review discusses the challenges and considerations associated with deploying machine learning in network security environments, such as data privacy, model interpretability, and adversarial attacks. Strategies for addressing these challenges and improving the robustness of machine learning models are explored. Finally, the review outlines future research directions and opportunities for leveraging machine learning to enhance network security. Areas such as federated learning, adversarial machine learning, and explainable AI are identified as promising avenues for further investigation. In summary, this comprehensive review provides insights into the potential of machine learning in enhancing network security and threat detection. By leveraging the capabilities of machine learning algorithms and techniques, organizations can strengthen their defenses against cyber threats and better protect their networks and sensitive data.
Keywords:
Machine Learning; Network; Security; Threat; Detection
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0