Social closeness based clone attack detection for mobile healthcare system

The inclusion of embedded sensors in mobile phones, and the explosion of their usage in people's daily lives provide users with the ability to collectively sense the world. The collected sensing data from such a mobile phone enabled social network can be mined for users' behaviors and their social communities, and to support a broad range of applications including mobile healthcare systems. However, such mobile healthcare systems built upon social networks are vulnerable to clone attacks, in which the adversary replicates the legitimate nodes and distributes the clones throughout the network to undermine the successful application deployment. Existing clone attack mitigation approaches either only focus on the prevention techniques or can only work in static or well-connected networks, and hence are not applicable to our targeted mobile healthcare systems. In this paper, we propose a social closeness based method in a mobile healthcare disease control system to detect any clone attacks that may be launched to disrupt the normal operations of the system. Our social closeness based method exploits the social relationships among users for clone attack detection. Specifically, we define a new metric called community betweenness, which considers mobile users' community information. We find that the value of this metric changes significantly under the clone attack, which is suitable to be used for clone attack detection. We derive both analytical and training based approaches to determine the threshold setting of the community betweenness for robust clone attack detection. Extensive trace-driven simulation studies reveal that our social closeness based method can detect clone attacks with high detection ratio and low false positive rate.