Home-based Internet of Things (IoT) devices have gained in popularity and many households became "smart" by using devices such as smart sensors, locks, and voice-based assistants. Given the limitations of existing authentication techniques, we explore new opportunities for user authentication in smart home environments. Specifically, we design a novel authentication method based on behavioral features extracted from user interactions with IoT devices. We perform an IRB-approved user study in the IoT lab at our university over a period of three weeks. We collect network traffic from multiple users interacting with 15 IoT devices in our lab and extract a large number of features to capture user activity. We experiment with multiple classification algorithms and also design an ensemble classifier with two models using disjoint set of features. We demonstrate that our ensemble model can classify six users with 86% accuracy, and five users with 97% accuracy.
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