Cross-device free-text keystroke dynamics authentication using federated learning

Free-text keystroke dynamics, the unique typing patterns of an individual, have been applied for the security of mobile devices by providing the non-intrusive and continuous user authentication. Existing authentication approaches mainly concentrate on the keystroke dynamics when operating a specific device, and overlook the generality of keystroke dynamics for cross-device user authentication. To tackle this problem, in this paper, we propose an efficient federated free-text keystroke dynamics mechanism to mitigate the difference in keyboards for cross-device authentication. Specifically, we explore and analyze the keystroke features of various keyboards and extract cross-device keystroke features. To protect user privacy, their type of rhythm information must be kept locally. We utilize federated learning based on the auxiliary model to train the authentication model. Our proposed solution was evaluated on a large-scale data set with 168,000 users. The experimental results show that our proposed solution performs well with great robustness across different types of keyboards.