DATA SCIENCE & SYSTEM SECURITY
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Computer systems generate a huge number of heterogeneous logs which provide rich contextual information about system status and are critical sources for system monitoring and diagnosis. Manually interpreting those logs is not effective, however, due to their large volume and complicated domain-specific syntax and semantic knowledge.
Anomaly detection is essential for identifying unusual system behaviors and has wide-ranging applications, from fraud detection to system monitoring. In web servers, anomalies are typically detected using two types of data: metrics (numerical indicators of performance) and logs (records of system events).
Administrators of most user-facing systems depend on periodic log data to get an idea of the health and status of production applications. Logs report information, which is crucial to diagnose the root cause of complex problems. In this paper, we present a real-time log analysis system called LogLens