Toward Robust and Effective Behavior Based User Authentication With Off-the-Shelf Wi-Fi

Behavior-based Wi-Fi user authentication has gained popularity in user-centered smart systems. However, its wide adoption has been hindered by certain critical issues, including significant performance degradation when the environment changes, the inability to handle unknown activities, and weak security due to basing authentication on the recognition of a single, one-off activity. In this paper, we propose Wi-Dist, which authenticates a user using a behavior password, i.e. a pre-chosen sequence of activities. Wi-Dist addressed the previously mentioned technical challenges through a cross-layer joint optimization framework. In particular, we address environment dependency by incorporating adversarial learning and optimizing both the signal layer and the domain adaptation layer. This enhances the performance of the learned model across various environments. To effectively handle unknown behaviors, we utilize an adversarial learning-based network. This network establishes a pseudo-decision boundary between samples from known and unknown sources, ensuring robust authentication. Additionally, for authentication using continuous activities, we employ double-sliding windows activity monitoring. This approach, coupled with activity state correction, partitions activities for accurate recognition. We also conducted extensive experiments in indoor environments to demonstrate that Wi-Dist is effective and robust.