MDSClone: Multidimensional scaling aided clone detection in internet of things

Po Yen Lee, Chia Mu Yu*, Tooska Dargahi, Mauro Conti, Giuseppe Bianchi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Cloning 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.

Original languageEnglish
Pages (from-to)2031-2046
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Volume13
Issue number8
DOIs
StatePublished - Aug 2018

Keywords

  • Clone attack
  • Internet of things
  • Multidimensional scaling
  • Network security

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