Log-based Anomaly Detection Without Log Parsing

Understand

Software systems often record important runtime information in system logs for troubleshooting purposes.

• There have been many studies that use log data to construct machine learning models for detecting system anomalies.
• Through our empirical study, we find that existing log-based anomaly detection approaches are significantly affected by log parsing errors that are introduced by 1) OOV (out-of-vocabulary) words, and 2) semantic misunderstandings.
• The log parsing errors could cause the loss of important information for anomaly detection.