论文标题
物联网的黑暗（和明亮）一面：识别智能家居设备和服务的攻击和对策
The Dark (and Bright) Side of IoT: Attacks and Countermeasures for Identifying Smart Home Devices and Services
论文作者
论文摘要
我们提出了一种基于机器学习的新攻击，该攻击利用网络模式来检测智能物联网设备的存在和在WiFi无线电频谱中运行服务。我们执行了广泛的数据收集测量活动，并建立了一个模型，描述了特征三个流行的物联网智能家居设备的流量模式，即Google Nest Mini，Amazon Echo和Amazon Echo Dot。我们证明，在拥挤的WiFi场景中，有可能检测并以压倒性的概率和上述设备运行的服务。这项工作证明，仅标准加密技术就不足以保护最终用户的隐私，因为网络流量本身揭示了设备和相关服务的存在。虽然需要更多的工作来防止未经信任的第三方检测和识别用户的设备，但我们引入了Eclipse，这是一种减轻这些类型攻击的技术，这可以重塑流量，从而使设备的标识和相关服务类似于随机分类基线。
We present a new machine learning-based attack that exploits network patterns to detect the presence of smart IoT devices and running services in the WiFi radio spectrum. We perform an extensive measurement campaign of data collection, and we build up a model describing the traffic patterns characterizing three popular IoT smart home devices, i.e., Google Nest Mini, Amazon Echo, and Amazon Echo Dot. We prove that it is possible to detect and identify with overwhelming probability their presence and the services running by the aforementioned devices in a crowded WiFi scenario. This work proves that standard encryption techniques alone are not sufficient to protect the privacy of the end-user, since the network traffic itself exposes the presence of both the device and the associated service. While more work is required to prevent non-trusted third parties to detect and identify the user's devices, we introduce Eclipse, a technique to mitigate these types of attacks, which reshapes the traffic making the identification of the devices and the associated services similar to the random classification baseline.
