Survey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure

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aut.relation.endpage2415
aut.relation.issue5
aut.relation.journalSensors
aut.relation.startpage2415
aut.relation.volume23
dc.contributor.authorPinto, Andrea
dc.contributor.authorHerrera, Luis-Carlos
dc.contributor.authorDonoso, Yezid
dc.contributor.authorGutierrez, Jairo A
dc.date.accessioned2023-03-06T02:56:02Z
dc.date.available2023-03-06T02:56:02Z
dc.date.copyright2023-02-22
dc.description.abstractIndustrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These infrastructures are not insulated anymore, and their connection to fourth industrial revolution technologies has expanded the attack surface. Thus, their protection has become a priority for national security. Cyber-attacks have become more sophisticated and criminals are able to surpass conventional security systems; therefore, attack detection has become a challenging area. Defensive technologies such as intrusion detection systems (IDSs) are a fundamental part of security systems to protect CI. IDSs have incorporated machine learning (ML) techniques that can deal with broader kinds of threats. Nevertheless, the detection of zero-day attacks and having technological resources to implement purposed solutions in the real world are concerns for CI operators. This survey aims to provide a compilation of the state of the art of IDSs that have used ML algorithms to protect CI. It also analyzes the security dataset used to train ML models. Finally, it presents some of the most relevant pieces of research on these topics that have been developed in the last five years.
dc.identifier.citationSensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 23(5), 2415-2415. doi: 10.3390/s23052415
dc.identifier.doi10.3390/s23052415
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/10292/15940
dc.languageen
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/1424-8220/23/5/2415
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4604 Cybersecurity and Privacy
dc.subject9 Industry, Innovation and Infrastructure
dc.subject0301 Analytical Chemistry
dc.subject0502 Environmental Science and Management
dc.subject0602 Ecology
dc.subject0805 Distributed Computing
dc.subject0906 Electrical and Electronic Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.titleSurvey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure
dc.typeJournal Article
pubs.elements-id495380
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