  Log analysis is an important technique that engineers use for troubleshooting
faults of large-scale service-oriented systems. In this study, we propose a
novel semi-supervised log-based anomaly detection approach, LogDP, which
utilizes the dependency relationships among log events and proximity among log
sequences to detect the anomalies in massive unlabeled log data. LogDP divides
log events into dependent and independent events, then learns normal patterns
of dependent events using dependency and independent events using proximity.
Events violating any normal pattern are identified as anomalies. By combining
dependency and proximity, LogDP is able to achieve high detection accuracy.
Extensive experiments have been conducted on real-world datasets, and the
results show that LogDP outperforms six state-of-the-art methods.
