利用后向散射检测机器人网络中的Sybil合谋攻击
Detecting Colluding Sybil Attackers in Robotic Networks using Backscatters
摘要：
由于无线介质的开放性，由许多小型机器人组成的机器人网络很容易受到Sybil攻击者的攻击，他们可以制造出无数的虚拟机器人。这种有害的攻击可能会推翻机器人协作中的基本信任假设，从而阻碍机器人网络在许多协作任务中的广泛部署。现有的解决方案依赖于庞大的多天线系统来被动地获得细粒度的物理层签名，这使得小型机器人无法负担。为了克服这一限制，我们提出了ScatterID，一个轻量级的系统，重视羽毛轻和无电池的反向散射标签的单天线机器人Sybil攻击缓解。ScatterID不是被动地“观察”签名，而是通过利用反向散射标签来主动地“操纵”多径传播，以有意地创建单天线机器人可获得的丰富的多径签名。特别是，这些签名被用来仔细构造相似向量，以挫败高级Sybil攻击者，后者进一步触发功率缩放和合谋攻击以生成不同的签名。然后，定制的随机森林模型的发展，以准确地推断每个机器人的身份合法性。我们在iRobot Create平台上实现了ScatterID，并在现实环境中的各种Sybil攻击下对其进行了评估。实验结果表明，ScatterID在基本和高级Sybil攻击下的AUROC高达0.987，总体准确率为95.4%。具体来说，它可以成功地检测到96.1%的假机器人，而错误地拒绝只有5.7%的合法机器人。
英文摘要：
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.
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