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All Journal Journal of Applied Engineering and Technological Science (JAETS)
P, Somasundari
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Enhancing Cybersecurity Threat Detection Using Machine Learning: A Comprehensive Review P, Somasundari; V, Kavitha
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.8814

Abstract

Cybersecurity forms the backbone of digital infrastructure that protects overstretched payment systems, governmental operations, and business continuity today. With machine learning (ML) techniques, it can help analyze a large amount of data and improve cyber-security. It’s tough to quantify how effective the ML-based cybersecurity system is, especially when we theorize it. This review paper talks about the significant role of ML in security, threat detection and security measures. Using machine learning algorithms helps in cybersecurity as they make the system automatic and fast. We can implement a threat detection security model using widely used ML algorithms. For classification purposes, we have Support Vector Machines (SVM), Decision Trees (DT), Random forests (RF), and Adaptive and Extreme gradient boosting (XGBoost). This review paper proposes ML algorithms for the implementation of cybersecurity with some practical application demonstrations. Machine learning algorithms can provide valuable analytics to help bolster security and reduce threats. We assess the accuracy of threat detection in network security by utilizing a set of formulas based on confusion, recall, F1-score, time complexity, accuracy and precision. This review synthesizes algorithmic performance across benchmark datasets (CICIDS2017 NSL-KDD UNSW-NB15) to identify significant gaps in previous ML-based cybersecurity frameworks. The results demonstrate the superior precision (90. 8 percent) and scalability of XGBoost.
Co-Authors V, Kavitha