躲猫猫：我看到你的灵巧家庭活动，甚至加密！
Peek-a-Boo: I see your smart home activities, even encrypted!
摘要：
灵巧家庭环境中的灯泡、开关、扬声器等无数物联网设备使用户能够轻松控制周围的物理世界，并通过这些设备中已经嵌入的传感器促进他们的生活方式。传感器数据包含大量关于用户和设备的敏感信息。然而，在灵巧家庭环境内部或附近的攻击者可能潜在地利用这些设备所使用的固有无线介质来从加密的有效载荷中泄露敏感信息（即，传感器数据），侵犯用户隐私。考虑到这一点，在这项工作中，我们介绍了一种新的多阶段的隐私攻击，在智能环境中对用户隐私。它是利用最先进的机器学习方法来实现的，用于检测和识别物联网设备的类型、它们的状态以及级联方式中正在进行的用户活动，方法是仅被动地嗅探来自灵巧家庭设备和传感器的网络流量。该攻击对加密和未加密的通信都有效。我们评估了攻击的效率与实际测量从一组广泛的流行的现成的灵巧家庭物联网设备利用一组不同的网络协议，如WiFi，ZigBee和BLE。我们的研究结果表明，对手被动嗅探流量可以达到非常高的准确性（90%以上），在确定目标灵巧家庭设备及其用户的状态和动作。为了防止这种隐私泄露，我们还提出了一种对策，基于生成欺骗流量来隐藏设备状态，并证明它提供了比现有解决方案更好的保护。
英文摘要：
A myriad of IoT devices such as bulbs, switches, speakers in a smart home environment allow users to easily control the physical world around them and facilitate their living styles through the sensors already embedded in these devices. Sensor data contains a lot of sensitive information about the user and devices. However, an attacker inside or near a smart home environment can potentially exploit the innate wireless medium used by these devices to exfiltrate sensitive information from the encrypted payload (i.e., sensor data) about the users and their activities, invading user privacy. With this in mind,in this work, we introduce a novel multi-stage privacy attack against user privacy in a smart environment. It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying the types of IoT devices, their states, and ongoing user activities in a cascading style by only passively sniffing the network traffic from smart home devices and sensors. The attack effectively works on both encrypted and unencrypted communications. We evaluate the efficiency of the attack with real measurements from an extensive set of popular off-the-shelf smart home IoT devices utilizing a set of diverse network protocols like WiFi, ZigBee, and BLE. Our results show that an adversary passively sniffing the traffic can achieve very high accuracy (above 90%) in identifying the state and actions of targeted smart home devices and their users. To protect against this privacy leakage, we also propose a countermeasure based on generating spoofed traffic to hide the device states and demonstrate that it provides better protection than existing solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
