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Contextual anomaly detection for a critical industrial system based on logs and metrics
conference contribution
posted on 2018-01-01, 00:00 authored by M Farshchi, I Weber, R Dellacorte, A Pecchia, M Cinque, Jean-Guy SchneiderJean-Guy Schneider, John GrundyRecent 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.
History
Event
Event Based Systems in Iaşi project. Conference (14th : 2018 : Iaşi, Romania)Series
Event Based Systems in Iaşi project ConferencePagination
140 - 143Publisher
Institute of Electrical and Electronics EngineersLocation
Iaşi, RomaniaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2018-09-10End date
2018-09-14ISBN-13
9781538680605Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2018, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
EDCC 2018 : Proceedings of the 2018 14th European Dependable Computing ConferenceUsage metrics
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