Anomaly detection in windows operating system through machine learning

Abstract

One of the main challenges of the new computer world is dealing with anomalies. This nature came to the computer without knowing it. Log files are crucial for detecting and mitigating anomalies in computer systems. Traditional human inspection approaches and rule-based systems become inadequate for log-based anomaly identification as the number and complexity of logs created by contemporary software systems rise. Machine learning approaches have emerged as interesting options for detecting anomalies in log files to overcome this obstacle. This study focuses on the creation of an anomaly detection mechanism for Windows operating system using machine learning. Our methodology offers significant advantages over existing rule-based methods for Windows operating system log analysis by integrating machine learning techniques. It provides a proactive defence against cyber-attacks and enables early identification and reaction to security risks. In addition, our methodology permits the discovery of previously unknown or undetected dangers, so enhancing the overall security posture of computer systems. Our effort contributes to the field of anomaly identification in Windows operating system and emphasizes the significance of log analysis for detecting and mitigating security threats.