Adaptive anomaly detection system based on machine learning algorithms in an industrial control environment
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F21%3A63536438" target="_blank" >RIV/70883521:28140/21:63536438 - isvavai.cz</a>
Result on the web
<a href="https://reader.elsevier.com/reader/sd/pii/S187454822100038X?token=15FAFBD82AC5A3ACC083A63D4C7966F575F42B0CD5F14F230D19DAC75D1307C27E28BDB25E01E305D6EB24C4734FDA7D&originRegion=eu-west-1&originCreation=20220312110530" target="_blank" >https://reader.elsevier.com/reader/sd/pii/S187454822100038X?token=15FAFBD82AC5A3ACC083A63D4C7966F575F42B0CD5F14F230D19DAC75D1307C27E28BDB25E01E305D6EB24C4734FDA7D&originRegion=eu-west-1&originCreation=20220312110530</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ijcip.2021.100446" target="_blank" >10.1016/j.ijcip.2021.100446</a>
Alternative languages
Result language
angličtina
Original language name
Adaptive anomaly detection system based on machine learning algorithms in an industrial control environment
Original language description
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
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Critical Infrastructure Protection
ISSN
1874-5482
e-ISSN
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Volume of the periodical
34
Issue of the periodical within the volume
2021
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
11
Pages from-to
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UT code for WoS article
000697770600002
EID of the result in the Scopus database
2-s2.0-85110443335