Affiliations: Department of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, Ontario, Canada
Correspondence:
[*]
Corresponding author. Yosef Ashibani, Department of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, Ontario, L1G 0C5 Canada. E-mail: [email protected].
Abstract: Smartphones have now become ubiquitous for accessing and controlling home appliances in smart homes, a popular application of the Internet of Things. User authentication on smartphones is mostly achieved at initial access. However, without applying a continuous authentication process, the network will be susceptible to unauthorized users. This issue emphasizes the importance of offering a continuous authentication scheme to identify the current user of the device. This can be achieved by extracting information during smartphone usage, including application access patterns. In this paper, we present a flexible machine learning user authentication scheme for smart home networks based on smartphone usage. Considering that users may run their smartphone applications differently during different day time intervals as well as different days of the week, new features are extracted by considering this information. The scheme is evaluated on a real-world dataset for continuous user authentication. The results show that the presented scheme authenticates users with high accuracy.
Keywords: App interactions, user authentication, smart home network, classification, foreground apps
