LAUSR

Keystroke biometrics is a well-investigated dynamic behavioral methodology that utilizes the unique behavioral patterns of users to verify their identity when tapping keys. However, the performance of keystroke biometrics is unreliable due to its high error rate and low robustness. In this paper, we propose differential evolution and adversarial noise-based user authentication (DEANUA), which is a verification scheme for enhancing reliability by reducing the error rate and improving robustness. We investigate the current mainstream features and build a more comprehensive feature set that composed of 146 features. Then, we use a differential evolution method to select an optimized feature set. With the support vector regression method on this feature set, we achieve an equal error rate (EER) of 0.12660% and also a 31.25% energy consumption reduction rate. In this paper, the model is trained with the training samples collected from one situation, but the model is used in various situations. Thus, the robustness of the model is inadequate. We constructed the adversarial noise samples to simulate users’ behavioral characteristics in different situational contexts. We use the adversarial noise samples to test the models in a strict experimental environment, which raises the EER by 83.59%, to 10.9299%. Then, we enhance the model with adversarial noise samples to obtain an EER of 8.70932%, which is a reduction of 20.32%.