Anomalous behavior detection-based approach for authenticating smart home system users

This paper presents Duenna, an authentication framework for smart home systems (SHSs). When using controlling apps (e.g., a smartphone app), Duenna makes sure that only legitimate SHS users are allowed to operate their Internet of things (IoT) devices. Duenna is built upon a behavioral anomaly detection (BAD)-based approach. In particular, we hypothesize that SHS users usually operate their home IoT devices in typical and distinctive patterns. Therefore, users that attempt to operate devices differently from such a regular behavior are considered malicious. Technically, Duenna operates in two modes. In an initialization operation, Duenna first collects and processes the historical cyber and physical activities of an SHS user in addition to the historical states of the SHS itself to build a set of incremental anomaly detection (AD) models. Then, in an interactive operation, the trained AD models are, then, used as a baseline from which anomalous commands (i.e., outliers) are detected and rejected, while regular commands (i.e., targets) are considered legitimate and allowed to be executed. Through an empirical evaluation conducted on real-world data, Duenna exhibits high authentication rates ensuring both security and user experience. The findings obtained from such evaluation show that a user behavior-based approach is a promising security scheme that could be integrated into existing SHS platforms.