Log Anomaly Mining System based on Machine Learning

Abstract

With the development of information technology, a large amount of log data is generated in the background of various information systems and equipment. There are different types of abnormal and fault information. The traditional detection methods usually rely on manual statistical discovery, or match by regular expression. An abnormal anomaly system based on artificial intelligence is constructed here, through the extraction of log templates, log clustering and log classification technology, to find abnormal events and fault information in the information system in time to avoid abnormal development and deterioration, reduce troubleshooting time and business interruption time to ensure the healthy operation of the system. The system has been actually tested and compared with the traditional log anomaly detection system. The results show that the depth of log analysis, the accuracy of event recognition, and the efficiency have been improved.