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Mobile fog computing security: A user-oriented smart attack defense strategy based on DQL

2020Journal article Restricted access
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dc.contributor.authorTu, Shanshan
dc.contributor.authorWaqas, Muhammad
dc.contributor.authorMeng, Yuan
dc.contributor.authorUr Rehman, Sadaqat
dc.contributor.authorAhmad, Iftekhar
dc.contributor.authorKoubaa, Anis
dc.contributor.authorHalim, Zahid
dc.contributor.authorHanif, Muhammad
dc.contributor.authorChang, Chin-Chen
dc.contributor.authorShi, Chengjie
dc.date.accessioned2020-10-30T09:28:06Z
dc.date.embargo2120
dc.date.issued2020
dc.description.abstractEach fog node interacts with data from multiple end-users in mobile fog computing (MFC) networks. Malicious users can use a variety of programmable wireless devices to launch different modes of smart attacks such as impersonation attack, jamming attack, and eavesdropping attack between fog servers and legitimate users. The existing research in MFC lacks in the contributions of defense of smart attack and also requires in the discussions of subjective decision making by participants. Therefore, we propose a smart attack defense scheme for authorized users in MFC in this paper. First, we construct a static zero-sum game model between smart attackers and legitimate users based on prospect theory. Second, the double Q-learning (DQL) is proposed to restrain the attack motive of smart attackers in the dynamic environment. The proposed DQL method generates the optimum defense choice of legitimate users against smart attacks so that they can efficiently determine whether to use only physical layer security (PLS) to avoid those smart attacks. We use our scheme to contrast with the basic schemes, i.e., Q-learning scheme, the Sarsa scheme, and the greedy strategy. Experiment results prove that the proposed scheme can enhance the utility of legitimate users, restrain the attack motive of smart attackers, and further provide better security protection in the MFC environment.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.comcom.2020.06.019pt_PT
dc.identifier.issn0140-3664
dc.identifier.urihttp://hdl.handle.net/10400.22/16373
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S014036642030253X?via%3Dihub#!pt_PT
dc.subjectMobile fog computingpt_PT
dc.subjectSmart attackpt_PT
dc.subjectProspect theorypt_PT
dc.subjectReinforcement learningpt_PT
dc.subjectGame theorypt_PT
dc.subjectPhysical layer securitypt_PT
dc.titleMobile fog computing security: A user-oriented smart attack defense strategy based on DQLpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage798pt_PT
oaire.citation.startPage790pt_PT
oaire.citation.titleComputer Communicationspt_PT
oaire.citation.volume160pt_PT
person.familyNameKoubaa
person.givenNameAnis
person.identifier989131
person.identifier.ciencia-idCA19-2399-D94A
person.identifier.orcid0000-0003-3787-7423
person.identifier.scopus-author-id15923354900
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0337d7df-5f77-46a4-8269-83d14bd5ea6b
relation.isAuthorOfPublication.latestForDiscovery0337d7df-5f77-46a4-8269-83d14bd5ea6b

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