As computer systems grow in scale,complexity,and user demands for higher quality of service,the importance of logging systems has increased significantly.Logs are records of data or events generated during the operation of computer systems,and abnormal data in log entries often indicate per-formance fluctuations,anomalies,or failures within the system.Existing research on log anomaly de-tection mostly relies on a single feature,leading to issues such as inefficiency,incompleteness,and high misjudgment rates.This paper proposes a multi-feature-based approach for detecting anomalies in log e-vents.Firstly,we define the multi-dimensional features of logs,including sequential,quantitative,se-mantic,and temporal features.Secondly,we utilize BERT combined with TF-IDF to obtain semantic feature vectors and integrate these features to form the input for our model.Finally,we establish a Bi-LSTM anomaly detection model based on an attention mechanism.Experiments show that the proposed anomaly detection model achieves a certain improvement in accuracy,providing a valuable reference for assisting in the discovery of log anomalies.
万方数据
维普
