CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection
  Method
Ling Chen Chaodu Song Xu Wang Dachao Fu Feifei Li
Anomaly detection based on system logs plays an important role in intelligent
operations, which is a challenging task due to the extremely complex log
patterns. Existing methods detect anomalies by capturing the sequential
dependencies in log sequences, which ignore the interactions of subsequences.
To this end, we propose CSCLog, a Component Subsequence Correlation-Aware Log
anomaly detection method, which not only captures the sequential dependencies
in subsequences, but also models the implicit correlations of subsequences.
Specifically, subsequences are extracted from log sequences based on components
and the sequential dependencies in subsequences are captured by Long Short-Term
Memory Networks (LSTMs). An implicit correlation encoder is introduced to model
the implicit correlations of subsequences adaptively. In addition, Graph
Convolution Networks (GCNs) are employed to accomplish the information
interactions of subsequences. Finally, attention mechanisms are exploited to
fuse the embeddings of all subsequences. Extensive experiments on four publicly
available log datasets demonstrate the effectiveness of CSCLog, outperforming
the best baseline by an average of 7.41% in Macro F1-Measure.