Anomaly Detection in Log Files Based on Machine Learning Techniques

Abstract

This article provides a comprehensive overview of contemporary techniques for detecting anomalies in log files in light of the growing reliance on computer systems and the volume of log files generated. Log files are crucial for identifying questionable or malicious activities since they shed light on system behavior and performance. The work addresses the challenges associated with identifying anomalies in log files, including their dynamic structure, high volume, and chaotic nature. Several anomaly detection strategies are assessed based on how well they work, how quickly they can be executed, and how well they can be applied to different types of log files. These strategies include statistical techniques, machine learning algorithms, and deep learning techniques. Furthermore, because cyber threats are getting more complex, AI applications are becoming crucial to network and cyber security. By utilizing anomaly detection, predictive analysis, and reactions to adjust to changing attack patterns, artificial intelligence can significantly enhance security.