Anomaly Identification in Cyber Logs Through Machine Learning Techniques
Ram Dwivedi, Nitesh Gupta, Anurag Shrivastava · INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND APPLIED SCIENCES · 2025
With the rising volume and sophistication of cyber attacks, traditional rule-based security systems have become insufficient to detect emerging and unknown threats. Machine Learning (ML) provides a dynamic and scalable solution for anomaly detection in cyber security logs, enabling systems to identify unusual patterns with minimal human intervention. This study offers a thorough framework that combines supervised, unsupervised, and semi-supervised models for anomaly identification using machine learning. Using the HDFS log dataset, we implement and evaluate Isolation Forest, Auto encoder, and Random Forest algorithms. The results demonstrate that ML techniques significantly enhance detection accuracy and reduce false positives, with Random Forest achieving the highest performance across all evaluation metrics.