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SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection

2023Journal article Open access
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dc.contributor.authorVitorino, João
dc.contributor.authorPraça, Isabel
dc.contributor.authorMaia, Eva
dc.date.accessioned2023-09-05T14:47:06Z
dc.date.available2023-09-05T14:47:06Z
dc.date.issued2023
dc.description.abstractMachine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in real ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their future experiments are adequate for a real communication network.pt_PT
dc.description.sponsorshipThe present work was partially supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within project ”Cybers SeC IP” (NORTE-01-0145-FEDER000044). This work has also received funding from UIDB/00760/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.doi10.1016/j.cose.2023.103433pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/23456
dc.language.isoengpt_PT
dc.relationNORTE-01-0145-FEDER000044pt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0167404823003437?via%3Dihub
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectRealistic adversarial examplespt_PT
dc.subjectAdversarial robustnesspt_PT
dc.subjectCybersecuritypt_PT
dc.subjectIntrusion detectionpt_PT
dc.subjectMachine learningpt_PT
dc.titleSoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PT
oaire.citation.titleComputers & Securitypt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMachado Vitorino
person.familyNamePraça
person.familyNameMaia
person.givenNameJoão Pedro
person.givenNameIsabel
person.givenNameEva
person.identifierHlqCxhoAAAAJ
person.identifier299522
person.identifier.ciencia-id3312-592F-B628
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id4F14-EF83-C4B9
person.identifier.orcid0000-0002-4968-3653
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-8075-531X
person.identifier.ridK-8430-2014
person.identifier.scopus-author-id57579914600
person.identifier.scopus-author-id22734900800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication4435bb64-8a77-407b-ba18-4833d26e72ae
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublication47a108c4-cf8a-46f3-8954-90624174e8fc
relation.isAuthorOfPublication.latestForDiscovery47a108c4-cf8a-46f3-8954-90624174e8fc
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