Publikace UTB
Repozitář publikační činnosti UTB

Adaptive anomaly detection system based on machine learning algorithms in an industrial control environment

Repozitář DSpace/Manakin

Zobrazit minimální záznam


dc.title Adaptive anomaly detection system based on machine learning algorithms in an industrial control environment en
dc.contributor.author Vávra, Jan
dc.contributor.author Hromada, Martin
dc.contributor.author Lukáš, Luděk
dc.contributor.author Dworzecki, Jacek
dc.relation.ispartof International Journal of Critical Infrastructure Protection
dc.identifier.issn 1874-5482 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2021
utb.relation.volume 34
dc.type article
dc.language.iso en
dc.publisher Elsevier B.V.
dc.identifier.doi 10.1016/j.ijcip.2021.100446
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S187454822100038X
dc.subject cyber security en
dc.subject machine learning en
dc.subject critical information infrastructure en
dc.subject anomaly detection en
dc.subject industrial control system en
dc.description.abstract Technology has become an integral part of contemporary society. The current transition from an industrial society to an information society is accompanied by the implementation of new technologies in every part of human activity. Increasing pressure to apply ICT in critical infrastructure resulted in the creation of new vulnerabilities. Traditional safety approaches are ineffective in a considerable number of cases. Therefore, machine learning another evolutionary step that provides robust solutions for extensive and sophisticated systems. The article focuses on cybersecurity research for industrial control systems that are widely used in the field of critical information infrastructure. Moreover, cybernetic protection for industrial control systems is one of the most important security types for a modern state. We present an adaptive solution for defense against cyber-attacks, which also consider the specifics of the industrial control systems environment. Moreover, the experiments are based on four machine learning algorithms (artificial neural network, recurrent neural network LSTM, isolation forest, and algorithm OCSVM). The proposed anomaly detection system utilizes multiple techniques and processes as preprocessing techniques, optimization techniques, and processes required for result interpretation. These procedures allow the creation of an adaptable and robust system that meets the need for industrial control systems. © 2021 The Authors en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1010456
utb.identifier.obdid 43882904
utb.identifier.scopus 2-s2.0-85110443335
utb.identifier.wok 000697770600002
utb.source j-scopus
dc.date.accessioned 2021-08-10T07:48:39Z
dc.date.available 2021-08-10T07:48:39Z
dc.description.sponsorship Ministry of the Interior of the Czech Republic [VI20192022151]; UIUI A.I.Lab at the Faculty of AppliedInformatics, Tomas Bata University in Zlin; project "e-Infra-struktura CZ" (e-INFRA) [LM2018140]
dc.description.sponsorship LM2018140; Ministerstvo Vnitra České Republiky: VI20192022151
dc.rights Attribution 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Vávra, Jan
utb.contributor.internalauthor Hromada, Martin
utb.contributor.internalauthor Lukáš, Luděk
utb.fulltext.sponsorship This research was funded by the Ministry of the Interior of the Czech Republic under Project VI20192022151 ‘CIRFI 2019: Indication of critical infrastructure resilience failure. Moreover, this work was supported by the resources of UIUI A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz). Furthermore, computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures.
utb.wos.affiliation [Vavra, Jan; Hromada, Martin; Lukas, Ludek] Tomas Bata Univ Zlin, Zlin, Czech Republic; [Vavra, Jan] Nam T G Masaryka 5555, Zlin 76001, Czech Republic; [Dworzecki, Jacek] Univ Land Forces Wroclaw, Wroclaw, Poland; [Dworzecki, Jacek] Acad Police Force Bratislava, Bratislava, Slovakia; [Dworzecki, Jacek] Pomeranian Acad Slupsk, Slupsk, Poland
utb.scopus.affiliation Tomas Bata University in Zlin, Zlin, Czech Republic; nam. T. G. Masaryka 5555760 01, Zlin, Czech Republic; University of the Land Forces in Wroclaw, Poland; Academy of the Police Force in Bratislava, Slovakia; Pomeranian Academy in Slupsk, Poland
utb.fulltext.projects VI20192022151
utb.fulltext.projects LM2018140
Find Full text

Soubory tohoto záznamu

Zobrazit minimální záznam

Attribution 4.0 International Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je Attribution 4.0 International