Abstract：Point at the problem of syslogs anomaly detection in modern large-scale systems，an anomaly detection method CSCM based on automatic log analysis is proposed.This method combines the process of refinement analysis and multi-view exception extraction after pre-clustering to realize the anomaly detection of syslog.Firstly，the information entropy is introduced to extract the information in the log.Based on the Canopy pre-clustering process，the datasets of the overlapping subset are extracted to narrow the calculation range.Next，the spectral clustering is used for detailed analysis，and the pre-clustering results are combined to optimize initialization problem.Finally，by associating log analysis from different perspectives，the definition of explicit exceptions and implicit exceptions is proposed respectively.Based on the analysis of sparse-cluster centroid and the calculation of anomaly degree，the anomaly logs are identified.The experimental results show that the proposed method can effectively identify the outliers in the syslog.
2022, Vol. 43 
