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.

翻译：我们展示了一种新的机器学习式攻击,利用网络模式检测智能IoT装置的存在和无线网络无线电频谱中的运行服务。我们开展了广泛的数据收集测量运动,并构建了一个模型,描述三种广受欢迎的IoT智能家用装置(谷歌雀巢、亚马逊回声和亚马逊回声点)特征的交通模式。我们证明,在拥挤的无线网络情景下,极有可能探测和识别这些装置的存在和上述装置运行的服务。 这项工作证明,标准加密技术本身不足以保护最终用户的隐私,因为网络通信本身暴露了该装置和相关服务的存在。 虽然需要做更多的工作来防止不受信任的第三方探测和识别用户的装置,但我们引入了Eclipse,这是一种缓解这类攻击的技术,这种技术重塑了交通方式,使设备及相关服务与随机分类基线相似。