User Presence Inference via Encrypted Traffic of Wireless Camera in Smart Homes

Abstract EN

Wireless cameras are widely deployed in smart homes for security guarding, baby monitoring, fall detection, and so on.

Those security cameras, which are supposed to protect users, however, may in turn leak a user’s personal privacy.

In this paper, we reveal that attackers are able to infer whether users are at home or not, that is, the user presence, by eavesdropping the traffic of wireless cameras from distance.

We propose HomeSpy, a system that infers user presence by inspecting the intrinsic pattern of the wireless camera traffic.

To infer the user presence, HomeSpy first eavesdrops the wireless traffic around the target house and detects the existence of wireless cameras with a Long Short-Term Memory (LSTM) network.

Then, HomeSpy infers the user presence using the bitrate variation of the wireless camera traffic based on a cumulative sum control chart (CUSUM) algorithm.

We implement HomeSpy on the Android platform and validate it on 20 cameras.

The evaluation results show that HomeSpy can achieve a successful attack rate of 97.2%.