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dc.contributor.authorZolotukhin, Mikhail
dc.contributor.authorZhang, Di
dc.contributor.authorHämäläinen, Timo
dc.contributor.authorMiraghaei, Parsa
dc.date.accessioned2023-01-04T07:30:06Z
dc.date.available2023-01-04T07:30:06Z
dc.date.issued2023
dc.identifier.citationZolotukhin, M., Zhang, D., Hämäläinen, T., & Miraghaei, P. (2023). On Attacking Future 5G Networks with Adversarial Examples : Survey. <i>Network</i>, <i>3</i>(1), 39-90. <a href="https://doi.org/10.3390/network3010003" target="_blank">https://doi.org/10.3390/network3010003</a>
dc.identifier.otherCONVID_164836939
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/84747
dc.description.abstractThe introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to dynamically create and deploy multiple services which function under various requirements in different vertical sectors while operating on top of the same physical infrastructure. The recent progress in artificial intelligence and machine learning is theorized to be a potential answer to the arising resource allocation challenges. It is therefore expected that future generation mobile networks will heavily depend on its artificial intelligence components which may result in those components becoming a high-value attack target. In particular, a smart adversary may exploit vulnerabilities of the state-of-the-art machine learning models deployed in a 5G system to initiate an attack. This study focuses on the analysis of adversarial example generation attacks against machine learning based frameworks that may be present in the next generation networks. First, various AI/ML algorithms and the data used for their training and evaluation in mobile networks is discussed. Next, multiple AI/ML applications found in recent scientific papers devoted to 5G are overviewed. After that, existing adversarial example generation based attack algorithms are reviewed and frameworks which employ these algorithms for fuzzing stat-of-art AI/ML models are summarised. Finally, adversarial example generation attacks against several of the AI/ML frameworks described are presented.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofseriesNetwork
dc.rightsCC BY 4.0
dc.subject.other5G networks
dc.subject.otherartificial intelligence
dc.subject.otherdeep learning
dc.subject.otheradversarial machine learning
dc.subject.other5G cybersecurity knowledge base
dc.titleOn Attacking Future 5G Networks with Adversarial Examples : Survey
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202301041102
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTekniikkafi
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingfi
dc.contributor.oppiaineEngineeringen
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange39-90
dc.relation.issn2673-8732
dc.relation.numberinseries1
dc.relation.volume3
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights.accesslevelopenAccessfi
dc.subject.ysoverkkopalvelut
dc.subject.ysokyberturvallisuus
dc.subject.ysomatkaviestinpalvelut (telepalvelut)
dc.subject.ysoalgoritmit
dc.subject.ysotekoäly
dc.subject.ysotietoturva
dc.subject.ysomatkaviestinverkot
dc.subject.yso5G-tekniikka
dc.subject.ysokoneoppiminen
dc.subject.ysoverkkohyökkäykset
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p6624
jyx.subject.urihttp://www.yso.fi/onto/yso/p26189
jyx.subject.urihttp://www.yso.fi/onto/yso/p15079
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p5479
jyx.subject.urihttp://www.yso.fi/onto/yso/p12758
jyx.subject.urihttp://www.yso.fi/onto/yso/p29372
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p27466
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/network3010003
jyx.fundinginformationThe research did not receive specific funding but was performed as a part of the employment of the authors in Magister Solutions Ltd., Jyväskylä, Finland. The APC is also expected to be funded by Magister Solutions Ltd.
dc.type.okmA1


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