  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.
