LogBERT: Log Anomaly Detection via BERT

Understand

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.