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No file available [This article belongs to Volume - 58, Issue - 1]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-05-05-2026
Title : Unsupervised Machine Learning Approaches for Anomaly Detection in Large-Scale Data Systems
Sonali Kothari, , Sonali Patil, , Ranjana Kale, , Madhavi Nimkar (Darokar), , Shweta Koparde, , Deepa Abin,

Abstract : The fast growth in the scale of large-scale data systems in sectors like cybersecurity, finance, healthcare, and industrial surveillance has enhanced the necessity of powerful anomaly detecting methods that can operate without indicated data. This paper explores the use of unsupervised machine learning to detect anomalies in high-dimensional and large-scale unhomogeneous data. Four exemplary algorithms, namely: Isolation Forest, One-Class Support Vector Machine (OC-SVM), Local Outlier Factor (LO

Keywords : Unsupervised Machine Learning Approaches, Anomaly Detection, Scale Data Systems The, unsupervised, machine