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Detecting Colluding Sybil Attackers in Robotic Networks Using Backscatters
IEEE/ACM Transactions on Networking ( IF 3.0 ) Pub Date : 2021-01-14 , DOI: 10.1109/tnet.2020.3048126 Yong Huang 1 , Wei Wang 1 , Tao Jiang 1 , Qian Zhang 2
IEEE/ACM Transactions on Networking ( IF 3.0 ) Pub Date : 2021-01-14 , DOI: 10.1109/tnet.2020.3048126 Yong Huang 1 , Wei Wang 1 , Tao Jiang 1 , Qian Zhang 2
Affiliation
Due to the openness of wireless medium, robotic networks that consist of many miniaturized robots are susceptible to Sybil attackers, who can fabricate myriads of fictitious robots. Such detrimental attacks can overturn the fundamental trust assumption in robotic collaboration and thus impede widespread deployments of robotic networks in many collaborative tasks. Existing solutions rely on bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, making them unaffordable to miniaturized robots. To overcome this limitation, we present ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots for Sybil attack mitigation. Instead of passively “observing” signatures, ScatterID actively “manipulates” multipath propagation by exploiting backscatter tags to intentionally create rich multipath signatures obtainable to single-antenna robots. Particularly, these signatures are used to carefully construct similarity vectors to thwart advanced Sybil attackers, who further trigger power-scaling and colluding attacks to generate dissimilar signatures. Then, a customized random forest model is developed to accurately infer the identity legitimacy of each robot. We implement ScatterID on the iRobot Create platform and evaluate it under various Sybil attacks in real-world environments. The experimental results show that ScatterID achieves a high AUROC of 0.987 and obtains an overall accuracy of 95.4% under basic and advanced Sybil attacks. Specifically, it can successfully detect 96.1% of fake robots while mistakenly rejecting just 5.7% of legitimate ones.
更新日期：2021-01-14
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