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Cybersecurity for industrial control systems: A survey
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2020 (English)In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 89, article id 101677Article in journal (Refereed) Published
Abstract [en]

Industrial Control System (ICS) is a general term that includes supervisory control & data acquisition (SCADA) systems, distributed control systems (DCS), and other control system configurations such as programmable logic controllers (PLC). ICSs are often found in the industrial sectors and critical infrastructures, such as nuclear and thermal plants, water treatment facilities, power generation, heavy industries, and distribution systems. Though ICSs were kept isolated from the Internet for so long, significant achievable business benefits are driving a convergence between ICSs and the Internet as well as information technology (IT) environments, such as cloud computing. As a result, ICSs have been exposed to the attack vectors used in the majority of cyber-attacks. However, ICS devices are inherently much less secure against such advanced attack scenarios. A compromise to ICS can lead to enormous physical damage and danger to human lives. In this work, we have a close look at the shift of the ICS from stand-alone systems to cloud-based environments. Then we discuss the major works, from industry and academia towards the development of the secure ICSs, especially applicability of the machine learning techniques for the ICS cyber-security. The work may help to address the challenges of securing industrial processes, particularly while migrating them to the cloud environments.

Place, publisher, year, edition, pages
Elsevier Ltd , 2020. Vol. 89, article id 101677
Keywords [en]
Cloud computing, Cybersecurity, Industrial control system, Intrusion detection system, Machine learning, Computation theory, Computer crime, Distributed parameter control systems, Industrial plants, Intrusion detection, Learning systems, Man machine systems, Network security, Programmable logic controllers, SCADA systems, Cyber security, Distribution systems, Industrial control systems, Intrusion Detection Systems, Machine learning techniques, Programmable logic controllers (PLC), System configurations, Water treatment facilities, Industrial water treatment
Identifiers
URN: urn:nbn:se:kau:diva-76483DOI: 10.1016/j.cose.2019.101677ISI: 000508490300010OAI: oai:DiVA.org:kau-76483DiVA, id: diva2:1388107
Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-02-20

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CiteExportLink to record
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Citation style
  • apa
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  • vancouver
  • Other style
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  • de-DE
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