  Detecting anomalous events in online computer systems is crucial to protect
the systems from malicious attacks or malfunctions. System logs, which record
detailed information of computational events, are widely used for system status
analysis. In this paper, we propose LogBERT, a self-supervised framework for
log anomaly detection based on Bidirectional Encoder Representations from
Transformers (BERT). LogBERT learns the patterns of normal log sequences by two
novel self-supervised training tasks and is able to detect anomalies where the
underlying patterns deviate from normal log sequences. The experimental results
on three log datasets show that LogBERT outperforms state-of-the-art approaches
for anomaly detection.
