A Systematic Literature Review on Cyber Threat Detection Using Machine Learning Techniques: Cyber Threat, Algorithms, and Regional Perspectives

A Systematic Literature Review on Cyber Threat Detection Using Machine Learning Techniques: Cyber Threat, Algorithms, and Regional Perspectives | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 July 2025 V1 Latest version Share on A Systematic Literature Review on Cyber Threat Detection Using Machine Learning Techniques: Cyber Threat, Algorithms, and Regional Perspectives Authors : Krishneel Sundar 0009-0005-6360-6960 [email protected] , Pritika Reddy , Kaylash Chaudhary 0000-0002-2378-7745 , and Shaireen Khan Authors Info & Affiliations https://doi.org/10.22541/au.175335709.96206480/v1 217 views 81 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The rapid escalation in the scale and sophistication of cyber threats has intensified interest in leveraging machine learning (ML) for proactive cybersecurity defense. This systematic literature review (SLR), guided by the PRISMA methodology, synthesizes studies published between 2016 and 2024 to identify machine learning algorithms demonstrating the highest effectiveness in predicting cyber threats. Initially, 350 articles were retrieved from three academic databases (Google Scholar, IEEE Xplore, and ScienceDirect). After applying rigorous inclusion and exclusion criteria, 74 articles were selected for detailed analysis. This review identified five dominant categories of cyber threats: phishing, ransomware, denial-of-service (DoS), cloud-based intrusions, and supply chain attacks. Additionally, the review assessed the performance of various ML algorithms discussed in the literature. Random Forest emerged as the most frequently employed and consistently effective classification algorithm, followed by Decision Trees, Support Vector Machines (SVM), and Naive Bayes classifiers. While findings indicate significant global progress, the review emphasizes a notable research gap in underrepresented regions such as the South Pacific, where ML applications in cybersecurity remain limited. The outcomes of this review provide a foundation for future research aimed at developing adaptive, ML-driven cyber defense systems tailored to the specific needs of the South Pacific and similar regions globally. Overall, this study contributes to the field by highlighting critical intersections between machine learning and cybersecurity. Supplementary Material File (a systematic literature review on cyber threat detection using machine learning techniques cyber threat, algorithms, and regional perspectives.docx) Download 201.71 KB Information & Authors Information Version history V1 Version 1 24 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cyber threats cybersecurity machine learning algorithms machine learning Authors Affiliations Krishneel Sundar 0009-0005-6360-6960 [email protected] Fiji National University View all articles by this author Pritika Reddy Fiji National University View all articles by this author Kaylash Chaudhary 0000-0002-2378-7745 The University of the South Pacific View all articles by this author Shaireen Khan Fiji National University View all articles by this author Metrics & Citations Metrics Article Usage 217 views 81 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Krishneel Sundar, Pritika Reddy, Kaylash Chaudhary, et al. A Systematic Literature Review on Cyber Threat Detection Using Machine Learning Techniques: Cyber Threat, Algorithms, and Regional Perspectives. Authorea . 24 July 2025. DOI: https://doi.org/10.22541/au.175335709.96206480/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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