Advancements in cybersecurity and machine learning: A comprehensive review of recent research
Doctorate Division, Capitol Technology University/United States of America.
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 271–284
Article DOI: 10.30574/wjaets.2024.13.1.0416
Publication history:
Received on 02 August 2024; revised on 12 September 2024; accepted on 14 September 2024
Abstract:
The convergence of cybersecurity and machine learning (ML) has emerged as a pivotal area of research, promising significant advancements in the protection of digital assets. This paper presents a comprehensive review of recent research focused on the integration of machine learning techniques within cybersecurity frameworks. We analyze key developments, including anomaly detection, threat intelligence, and automated response systems. The review highlights both the benefits and challenges of employing ML in cybersecurity, such as enhanced threat detection capabilities and potential issues related to adversarial attacks. By synthesizing findings from various studies, this paper aims to provide a nuanced understanding of how machine learning is transforming cybersecurity practices and suggest future research directions to address existing gaps and enhance system robustness.
Keywords:
Adversarial attacks; Anomaly detection; Automated response; Cybersecurity; Machine learning
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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