Machine Learning for Network Anomaly Detection A Review

Abstract

This research aims to investigate the application of machine learning (ML) techniques in network anomaly detection to enhance security in the face of evolving cyber threats. Employing a systematic review of existing literature and experimental evaluation, the study explores the effectiveness of various ML algorithms and their capacity to detect anomalies in network traffic. Unlike traditional rule-based methods, ML algorithms analyze extensive traffic data to distinguish normal from abnormal behavior, adapting dynamically to new threats in real-time. Key methodologies include feature engineering to optimize model performance, focusing on attributes like packet size and flow duration. The research evaluates detection accuracy, reduction of false positives, and the adaptability of ML-based systems to changing conditions. Main outcomes demonstrate that ML offers significant advantages over heuristic approaches, with improved detection rates, minimized human intervention, and enhanced responsiveness to emerging threats. The findings underscore the importance of real-time detection capabilities and highlight challenges such as computational complexity and dataset quality. By addressing these challenges, the study contributes valuable insights into strengthening network defense mechanisms through advanced ML applications.