2018 14th European Dependable Computing Conference (EDCC) (2018)
Sep 10, 2018 to Sep 14, 2018
Recent advances in contextual anomaly detection attempt to combine resource metrics and event logs to uncover unexpected system behaviors at run-time. This is highly relevant for critical software systems, where monitoring is often mandated by international standards and guidelines. In this paper, we analyze the effectiveness of a metrics-logs contextual anomaly detection technique in a middleware for Air Traffic Control systems. Our study addresses the challenges of applying such techniques to a new case study with a dense volume of logs, and finer monitoring sampling rate. Guided by our experimental results, we propose and evaluate several actionable improvements, which include a change detection algorithm and the use of time windows on contextual anomaly detection.
air traffic control, middleware, safety-critical software, software metrics
M. Farshchi et al., "Contextual anomaly detection for a critical industrial system based on logs and metrics," 2018 14th European Dependable Computing Conference (EDCC), Ia?i, Romania, 2018, pp. 140-143.