Traffic Processing and Fingerprint Generation for Smart Home Device Event

Recent studies show that smart home devices are vulnerable to passive network observers, referred to as adversaries. An adversary can use mobile devices as a sniffer to infer device events (e.g., a door is opened/closed) even through encrypted WiFi traffic. However, to obtain high event identification accuracy, existing works heavily rely on either machine learning (ML) or large traffic feature sizes, which results in a costly retraining or pairing process. In this paper, we generate an event traffic fingerprint and propose an event identification method with good extensibility. To address the interference of network fluctuations and unrelated traffic, we filter all redundant and event-unrelated packets and extract event packet sequences based on time interval. After processing, we generate a representative packet-level fingerprint for each event and identify the event based on fingerprint matching. The precision and recall of event identification reach 96.77% and 92.31% on average. Our traffic processing method can work as an add-on to existing ML-based event inference methods, which leads to at least 12% increase on the accuracy.