Po-Yen, L, Chia-Mu, Y, Dargahi, T 
ORCID: https://orcid.org/0000-0002-0908-6483, Mauro, C and Giuseppe, B
2018,
'MDSClone : multidimensional scaling aided clone detection in Internet of Things'
, IEEE Transactions on Information Forensics and Security, 99
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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 (i) detects clones without the need to know the geographical positions of nodes, and (ii) 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 (iii) 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.
| Item Type: | Article |
|---|---|
| Schools: | Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre |
| Journal or Publication Title: | IEEE Transactions on Information Forensics and Security |
| Publisher: | IEEE |
| ISSN: | 1556-6013 |
| Related URLs: | |
| Funders: | Taiwan Ministry of Science and Technology, European Commission, EU-India, CNR-MOST/Taiwan 2016-2017, Cisco University Research Program Fund and Silicon Valley Community Foundation, Intel |
| Depositing User: | T Dargahi |
| Date Deposited: | 20 Feb 2018 10:09 |
| Last Modified: | 15 Feb 2022 22:53 |
| URI: | https://usir.salford.ac.uk/id/eprint/46093 |
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