Log Anomaly Detection Model Based on Hybrid Feature Balanced Graph Attention Network
The existing methods neglect the imbalance in log abnormal data and the correlation between log features,leading to the problem of low accuracy in anomaly detection.This paper proposes a log anomaly detection model based on a hybrid graph attention network with balanced features(HBGATLog).Firstly,the hybrid log graph construction module is established,it extracts the semantic information,log sequence and time structure of log data through a hybrid feature extraction module,which enhances the correlation between log features.In addition,a log graph construction module is employed to build a log graph,which can effectively preserve spatial structural features.Secondly,a balanced log graph generation module is designed to solve the problem that unbalanced log data leads to detection results biased towards the majority classes.Thirdly,the graph log anomaly detection module is used for anomaly detection.Finally,three public datasets,BGL,Thunderbird and HDFS,are used to validate HBGATLog,and the experimental results show that the F1 score reaches 99.0％,98.7％and 98.1％,respectively.It is proved that HBGATLog can not only solve the problem of log data imbalance,fully consider the correlation of log data features,but also effectively reduce the missing rate.
万方数据
维普
