Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870
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RESEARCH ARTICLE   (Open Access)
Contents Vol 6 (1)

Machine Learning-Based Anomaly Detection for Cyber Threat Prevention

Norun Nabi1, Mohammad Mizanur Rahman2, Suday Kumer Ghosh3, Sanjida Alam4, Rony Barua5, Md. Asaduzzaman6, Nahid Reza Shatu7

+ Author Affiliations

Journal of Primeasia 6 (1) 1-8 https://doi.org/10.25163/primeasia.6110172

Submitted: 07 January 2025 Revised: 20 March 2025  Published: 24 March 2025 

Abstract

This study investigates the effectiveness of machine learning-based anomaly detection systems for cyber threat prevention, employing a quantitative research design with primary data collected from 400 cybersecurity professionals, IT administrators, and network security experts. The study investigated the implementation, efficacy, and constraints of machine learning (ML) algorithms by means of statistical examination, regression analysis, and the utilization of methodologies including decision trees, random forests, and support vector machines. A structured questionnaire was used to gather primary data for the study and sample size was 400. Findings reveal that 68% of organizations utilize ML for threat detection, with the financial sector leading at 75%. Despite promising adoption rates, challenges such as high false positive rates (54%), zero-day threat detection difficulties (41%), and data imbalance (60%) persist. Real-time learning and better integration with security infrastructure were highlighted as crucial for improving threat detection accuracy and system adaptability. While 72% of respondents viewed ML as effective, most emphasized the need for enhanced interoperability, false positive management, and incident response automation. These revelations highlight the progressive function of machine learning within the cybersecurity domain and the critical necessity for ongoing system enhancement in anticipation of forthcoming threat environments.

Keywords: Machine Learning, Anomaly Detection, Threat Prevention, Zero-Day Threats, Security Integration, Incident Response Automation.

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