MDSClone : multidimensional scaling aided clone detection in Internet of Things

Po-Yen, L; Chia-Mu, Y; Dargahi, T; Mauro, C; Giuseppe, B

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.

Citation

Po-Yen, L., Chia-Mu, Y., Dargahi, T., Mauro, C., & Giuseppe, B. (2018). MDSClone : multidimensional scaling aided clone detection in Internet of Things. IEEE Transactions on Information Forensics and Security, 99, https://doi.org/10.1109/TIFS.2018.2805291

• Journal Article Type: Article
• Acceptance Date: Jan 25, 2018
• Online Publication Date: Feb 12, 2018
• Publication Date: Feb 12, 2018
• Deposit Date: Feb 20, 2018
• Publicly Available Date: Feb 20, 2018
• Journal: IEEE Transactions on Information Forensics and Security
• Print ISSN: 1556-6013
• Electronic ISSN: 1556-6021
• Publisher: Institute of Electrical and Electronics Engineers
• Volume: 99
• DOI: https://doi.org/10.1109/TIFS.2018.2805291
• Publisher URL: http://dx.doi.org/10.1109/TIFS.2018.2805291
• Related Public URLs: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10206
• Additional Information: 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; Projects : Marie Curie Fellowship;TagItSmart!;REACH;Verifiable Data Structure Streaming;Scalable IoT Management and Key security aspects in 5G systems;SYMBIOTE; Grant Number: MOST 106-3114-E-005-001; Grant Number: PCIG11-GA-2012-321980; Grant Number: H2020-ICT30-2015-688061; Grant Number: ICI+/2014/342-896; Grant Number: 2017-166478 (3696); Grant Number: 688156; Grant Number: MOST 106-2221-E-005-017; Grant Number: MOST 106-2218-E-155-007; Grant Number: MOST 104-2628-E-155-001-MY2; Grant Number: MOST 105-2923-E-001-002-MY2; Grant Number: MOST 105-2923-E-002-014-MY3; Grant Number: MOST 105-2218-E-155-010