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首页> 外文期刊>IEEE/ACM Transactions on Networking >Detecting Colluding Sybil Attackers in Robotic Networks Using Backscatters
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Detecting Colluding Sybil Attackers in Robotic Networks Using Backscatters

机译：在使用背拍检测机器人网络中的勾结Sybil攻击者

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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.
机译：由于无线介质的开放性，由许多小型化机器人组成的机器人网络易于Sybil攻击者，他们可以制造虚拟机器人的无数。这种有害的攻击可以推翻机器人协作中的基本信任假设，从而妨碍了机器人网络的广泛部署在许多协作任务中。现有的解决方案依赖于庞大的多天线系统以被动地获得细粒度的物理层签名，使其无法对小型机器人提供不适应。为了克服这种限制，我们呈现了一个轻量级系统，将羽毛和无限制的反向散射标签附加到单天线机器人以进行Sybil攻击缓解。而不是被动地“观察”签名，散塔积极地“操纵”多径传播通过利用反向散射标签来故意创建可获得的单天线机器人的丰富多径签名。特别地，这些签名用于仔细构建相似性向量，以挫败高级Sybil攻击者，他们进一步触发功率缩放和勾结攻击以产生异种差异。然后，开发了一种定制的随机森林模型，以准确地推断每个机器人的身份合法性。我们在Irobot创建平台上实施散域，并在现实世界环境中的各种Sybil攻击下进行评估。实验结果表明，散氏达到0.987的高氧化氢，并在基本和先进的Sybil攻击下获得95.4％的整体准确性。具体而言，它可以成功地检测96.1％的假机器人，同时错误地拒绝了合法的5.7％。

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