Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/99307
標題: MDSClone: Multidimensional Scaling Aided Clone Detection in Internet of Things
作者: Po-Yen Lee
Chia-Mu Yu
Tooska Dargahi
Mauro Conti
Giuseppe Bianchi
游家牧
關鍵字: Network security
clone attack
internet of things
multidimensional scaling
出版社: IEEE Transactions on Information Forensics and Security
摘要: 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.
URI: http://hdl.handle.net/11455/99307
文章連結: https://ieeexplore.ieee.org/document/8290715
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