Behavior biometrics-based user authentication with Wi-Fi gains significant attention due to its ubiquitous and contact-free manners. An individual’s identity can be verified by analyzing activities induced signal variances, excellently balancing the security demands and user experience. However, the inherent complexity of Wi-Fi signals presents significant challenges for behavior biometrics-based user authentication. The susceptibility of Wi-Fi signals results in a poor cross-environment generalization capability, which is overlooked by the existing research. In addition, most existing works of behavior-based user authentication are based on one-off activity. This makes them vulnerable to zero-effort attacks and imitation attacks. To address these issues, we propose a cross-environment continuous gesture-based user authentication framework with Wi-Fi, dubbed Wi-CGAuth. Specifically, the cross-environment generalization capability is enhanced by the cross-layer joint optimization approach. At the lowest signal layer, the signals’ time, spatial, and frequency diversity are extended maximally, by a novel, subcarrier-level, cost-effective signal optimization strategy. At the middle layer, the multi-view fusion method, i.e., multi-transfer component analysis (TCA), is applied to refine the signals from transceiver pairs after signal preprocessing. The continuous gesture segmentation problem is modeled as the classification problem, which is solved by CNN. At the upper layer, a Convolutional Neural Network-Transformer (CNN-Transformer) model is employed to achieve the dual task of effective user authentication and accurate gesture recognition. After extensive experiments in three typical indoor scenarios, Wi-CGAuth can achieve an average authentication accuracy of 92.7%, demonstrating its robustness and effectiveness.

The existing commodity Wi-Fi-based human gait recognition systems mainly focus on a single subject due to the challenges of multisubject walking monitoring. To tackle the problem, we propose Wi-Diag, the first commodity Wi-Fi-based multisubject abnormal gait diagnosis system that leverages only one pair of off-the-shelf commercial Wi-Fi transceivers to separate each subject’s gait information and maintains an excellent performance when the scenario changes. It is an intelligent multisubject gait diagnosis system that can release an experienced doctor from heavy load work. Multisubject abnormal gait diagnosis is modeled as a blind source separation (BSS) issue, and multisubject walking mixed signals are efficiently separated by IC analysis (ICA) approach. This fact is verified by comprehensive theoretical derivation and experimental validation. In addition, CycleGAN is leveraged to mitigate the environmental dependency so that Wi-Diag can be robust when the scenario changes. The excellent performance of Wi-Diag is verified by extensive experiments. The average mean diagnosis accuracy with a maximum group size of four and various scenarios is 87.77%.

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.