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dc.contributor.authorPo-Yen Leezh_TW
dc.contributor.authorChia-Mu Yuzh_TW
dc.contributor.authorTooska Dargahizh_TW
dc.contributor.authorMauro Contizh_TW
dc.contributor.authorGiuseppe Bianchizh_TW
dc.description.abstractCloning is a very serious threat in the Internet of Things (IoT), owing to the simplicity for an attacker to gather configuration and authentication credentials from a non-tamper-proof node, and replicate it in the network. In this paper, we propose MDSClone, a novel clone detection method based on multidimensional scaling (MDS). MDSClone appears to be very well suited to IoT scenarios, as it: 1) detects clones without the need to know the geographical positions of nodes; 2) unlike prior methods, it can be applied to hybrid networks that comprise both static and mobile nodes, for which no mobility pattern may be assumed a priori. Moreover, a further advantage of MDSClone is that 3) the core part of the detection algorithm can be parallelized, resulting in an acceleration of the whole detection mechanism. Our thorough analytical and experimental evaluations demonstrate that MDSClone can achieve a 100% clone detection probability. Moreover, we propose several modifications to the original MDS calculation, which lead to over a 75% speed up in large scale scenarios. The demonstrated efficiency of MDSClone proves that it is a promising method towards a practical clone detection design in IoT.zh_TW
dc.publisherIEEE Transactions on Information Forensics and Securityzh_TW
dc.relationIEEE Transactions on Information Forensics and Security ( Volume: 13 , Issue: 8 , Aug. 2018 )zh_TW
dc.subjectNetwork securityzh_TW
dc.subjectclone attackzh_TW
dc.subjectinternet of thingszh_TW
dc.subjectmultidimensional scalingzh_TW
dc.titleMDSClone: Multidimensional Scaling Aided Clone Detection in Internet of Thingszh_TW
dc.typeJournal Articlezh_TW
Appears in Collections:資訊科學與工程學系所


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