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Industrial Network Topology Analysis with Episode Mining

Meshram, Ankush

Industrial network communication is highly deterministic as result of availability requirement of control systems in automated industrial production systems. This deterministic character helps with initial step of self-learning anomaly detection systems to detect periodic production cycle in industrial network communication. The methods for frequent episode mining in event sequences fits well to solve the
challenge of production cycle detection for self-learning system. We encode the network communication events to serial and parallel episodes. Methods for discovery of frequent episodes in event sequences are briefly explained. These methods would be further adapted in future to our encoded network communication traffic to extract production cycle comprised of serial and parallel episodes.

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Verlagsausgabe §
DOI: 10.5445/IR/1000097094
Veröffentlicht am 02.08.2019
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2019
Sprache Englisch
Identifikator ISBN: 978-3-7315-0936-3
ISSN: 1863-6489
KITopen-ID: 1000097094
Erschienen in Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. Ed.: J. Beyerer, M. Taphanel
Verlag KIT Scientific Publishing, Karlsruhe
Seiten 47-53
Serie Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe ; 40
Bemerkung zur Veröffentlichung Technical Report IES-2018-04
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