Research on data-driven anomaly detection model based on Bi-LSTM
Jianqiao Sheng, Ming Li, Liang Zhang, Yong Ma, X W Yu · Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022) · 2022
In order to make full use of operation and maintenance records for in-depth mining to support management, improve operation and maintenance work management level and execution efficiency, this paper analyzes and preprocesses operation and maintenance log files by studying artificial intelligence technology. A user behavior expression model is constructed from each dimension and log analysis experiments are carried out. Through the research on LSTM and BiLSTM methods, a data-driven abnormal behavior intelligent analysis method based on Bi-LSTM is proposed. The experimental results show that the Bi-LSTM method in this paper has an accuracy of 86% and strong operability in detecting abnormal operation and maintenance operations, and has good performance in detecting abnormal operation and maintenance operations of users. The command line is used as an analysis of abnormal operation and maintenance behavior of audit data.