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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • 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

  • 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

  • UT code for WoS article

    000697770600002

  • EID of the result in the Scopus database

    2-s2.0-85110443335