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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, masked log message prediction and volume of hypersphere minimization. After training, LogBERT is able to capture the patterns of normal log sequences and further detect anomalies where the underlying patterns deviate from expected patterns. The experimental results on three log datasets show that LogBERT outperforms state-of-the-art approaches for anomaly detection.