A comparative evaluation of machine learning algorithms for network anomaly detection

Abstract

In this era of digital transformation, the importance of network anomaly detection has been amplified to safeguard the security and integrity of various vital applications. These applications include the protection of critical infrastructures, prevention of cyber-attacks, and upkeep of network performance, among others. Our study presents a thorough evaluation of an array of machine learning algorithms for the proficient detection of network anomalies. These algorithms include, but are not limited to, Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). We illustrate an in-depth comparison between these chosen algorithms, analyzing their performance metrics, strengths, and weaknesses, with a specific focus on their practical applicability and influence on network security methodologies. The investigation into these machine learning algorithms exhibits potential advantages and constraints of employing such methodologies for network anomaly detection. We further shed light on the determinants that affect their performance in diverse scenarios. Based on the exhaustive analysis, we provide suggestive guidelines for choosing the most suitable algorithm depending on specific use cases or requirements. This study thus serves as a comprehensive guide to understanding the role and impact of machine learning in the critical field of network anomaly detection.