The House That Knows You: User Authentication Based on IoT Data

Abstract

Home-based Internet of Things (IoT) devices have gained in popularity and many households have become 'smart' by using devices such as smart sensors, locks, and voice-based assistants. Traditional authentication methods such as passwords, biometrics or multi-factor (using SMS or email) are either not applicable in the smart home setting, or they are inconvenient as they break the natural flow of interaction with these devices. Voice-based biometrics are limited due to safety and privacy concerns. 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 five users with 0.97 accuracy. The behavioral authentication modules could help address the new challenges emerging with smart home ecosystems and they open up the possibility of creating flexible policies for authorization and access control.