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dc.contributor.authorVähäkainu, Petri
dc.contributor.authorLehto, Martti
dc.contributor.authorKariluoto, Antti
dc.date.accessioned2021-02-12T08:57:28Z
dc.date.available2021-02-12T08:57:28Z
dc.date.issued2020
dc.identifier.citationVähäkainu, P., Lehto, M., & Kariluoto, A. (2020). Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems. <i>Journal of Information Warfare</i>, <i>19</i>(4), 57-69. <a href="https://www.jinfowar.com/journal/volume-19-issue-4/adversarial-attack%E2%80%99s-impact-machine-learning-model-cyber-physical-systems" target="_blank">https://www.jinfowar.com/journal/volume-19-issue-4/adversarial-attack%E2%80%99s-impact-machine-learning-model-cyber-physical-systems</a>
dc.identifier.otherCONVID_42349663
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/74117
dc.description.abstractDeficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine Learning models used in a cloud data platform service. Adversarial examples are malicious inputs to ML-models that provide erroneous model outputs while appearing to be unmodified. This kind of attack can fool the classifier and can prevent ML-models from generalizing well and from learning high-level representation; instead, the ML-model learns superficial dataset regularity. This study focuses on investigating, detecting, and preventing adversarial attacks towards a cloud data platform in the cyber-physical context.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherPeregrine Technical Solutions
dc.relation.ispartofseriesJournal of Information Warfare
dc.relation.urihttps://www.jinfowar.com/journal/volume-19-issue-4/adversarial-attack%E2%80%99s-impact-machine-learning-model-cyber-physical-systems
dc.rightsIn Copyright
dc.subject.otherArtificial Intelligence
dc.subject.othercloud data platform
dc.subject.otheradversarial attacks
dc.subject.otherdefence mechanisms
dc.subject.othermachine learning
dc.titleAdversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202102121545
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.format.pagerange57-69
dc.relation.issn1445-3312
dc.relation.numberinseries4
dc.relation.volume19
dc.type.versionpublishedVersion
dc.rights.copyright© Peregrine Technical Solutions, 2020
dc.rights.accesslevelopenAccessfi
dc.subject.ysoesineiden internet
dc.subject.ysokyberturvallisuus
dc.subject.ysotekoäly
dc.subject.ysopilvipalvelut
dc.subject.ysoverkkohyökkäykset
dc.subject.ysotietoturva
dc.subject.ysokoneoppiminen
dc.subject.ysoälytekniikka
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p27206
jyx.subject.urihttp://www.yso.fi/onto/yso/p26189
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p24167
jyx.subject.urihttp://www.yso.fi/onto/yso/p27466
jyx.subject.urihttp://www.yso.fi/onto/yso/p5479
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p27260
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en


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