Et Tu Alexa? When Commodity WiFi Devices Turn into Adversarial Motion Sensors
Et Tu Alexa? When Commodity WiFi Devices Turn into Adversarial Motion Sensors
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DOI：
10.14722/ndss.2020.23053
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Yanzi Zhu;Zhujun Xiao;Yuxin Chen;Zhijing Li;Max Liu;Ben Y. Zhao;Haitao Zheng
中科院分区：
文献类型：
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作者：
Yanzi Zhu;Zhujun Xiao;Yuxin Chen;Zhijing Li;Max Liu;Ben Y. Zhao;Haitao Zheng
我们的工作展示了一套新的无声侦察攻击，它利用商用 WiFi 设备来跟踪私人家庭和办公室内的用户，而不会损害任何 WiFi 网络、数据包或设备。我们表明，只需嗅探现有的 WiFi 信号，攻击者就可以准确地检测和跟踪建筑物内用户的活动。这是通过我们的新信号模型实现的，该模型将 WiFi 发射器附近的人体运动与攻击者在房产外部嗅探器看到的多路径信号传播的变化联系起来。由此产生的攻击成本低廉、高效，但难以检测。我们使用单一商用智能手机实施攻击，将其部署在 11 个现实世界的办公室和住宅公寓中，并证明其非常有效。最后，我们评估了潜在的防御措施，并提出了一种基于 AP 信号混淆的实用且有效的防御措施。
Our work demonstrates a new set of silent reconnaissance attacks, which leverages the presence of commodity WiFi devices to track users inside private homes and offices, without compromising any WiFi network, data packets, or devices. We show that just by sniffing existing WiFi signals, an adversary can accurately detect and track movements of users inside a building. This is made possible by our new signal model that links together human motion near WiFi transmitters and variance of multipath signal propagation seen by the attacker sniffer outside of the property. The resulting attacks are cheap, highly effective, and yet difficult to detect. We implement the attack using a single commodity smartphone, deploy it in 11 real-world offices and residential apartments, and show it is highly effective. Finally, we evaluate potential defenses, and propose a practical and effective defense based on AP signal obfuscation.