Network Abnormal Log Clustering Algorithm Based on DBSCAN

In the current information age, network security issues are becoming increasingly prominent. The vast amount of log data generated by network systems carries crucial information that aids in detecting security threats and abnormal behavior. This research proposes a log clustering method based on the DBSCAN algorithm, aimed at identifying anomalies within networks. We introduce the characteristics of network log data, discuss the limitations of traditional methods, and provide a detailed description of our algorithm and its application in log analysis. This algorithm effectively identifies clusters of different densities and enhances the accurate detection of network security events. Through experiments, we have verified the effectiveness of this method, reducing false positives and improving the efficiency of detecting network security events. Finally, we discuss future research directions, providing valuable insights for the field of network security.