融合语义特征的日志异常检测方法研究OA

With the continuous expansion of system functionalities,the volume of system logs has grown exponentially,presenting substantial challenges to conventional anomaly detection approaches.Deep learning-based log anomaly detection techniques have gradually become a research hotspot due to their powerful feature extraction capabilities.This study proposes a semi-supervised log anomaly detection model LogSem,which integrates semantic features.By introducing log content vectors that contain semantic information of the main log content and incorporating masked log key prediction tasks and hypersphere volume minimization tasks for semi-supervised learning,the model deeply explores the semantic features of logs.Experiments conducted on three mainstream datasets show that the proposed method outperforms the LogBERT baseline model in terms of the F1 score.Furthermore,this study explores and verifies the feasibility of addressing the out-of-vocabulary problem through semi-supervised learning.