ISSN: 2582-8266 (Online) || ISSN Approved Journal || Google Scholar Indexed || Impact Factor: 9.48 || Crossref DOI
How machine learning is transforming cyber threat detection
1 Department of Computer Science, Louisiana State University Shreveport, Shreveport, USA.
2 Computer Science and Engineering, Stamford University Bangladesh.
3 SBIT Inc., USA.
4 Department of Computer Science and Engineering, Daffodil International University Dhaka Bangladesh.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(02), 963-973.
Article DOI: 10.30574/wjaets.2024.13.2.0581
Publication history:
Received on 12 October 2024; revised on 25 November 2024; accepted on 29 November 2024
Abstract:
Using machine learning (ML) has made it faster and more precise to discover cyber security threats. Older methods of detecting threats usually struggle with today’s attack volume and complexity which causes delays and can result in mistakes. ML technology helps security teams notice known and new threats in a much shorter period than manual detection. Adopting supervised and unsupervised model types, they can adapt to any new kinds of attacks, raising the chance of detecting them with fewer errors. This study assesses different ML tools and explores when they show better outcomes than standard security systems. The analysis shows that including ML in cyber defense plans increases how well we detect threats, responds to security incidents and safeguards the organization. The findings recommend that companies rely heavily on smart and automated tools for threat detection in cyber security.
Keywords:
Cybersecurity threats; Machine learning; Threat detection; Data analysis; Supervised learning; Incident response
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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