B2CAR: Behavioural Biometrics for Continuous Authentication with Regularisation Techniques
Mustafa Al Samara, Marc Gilg, Abdelhafid Abouaïssa, Ismail Bennis, Pascal Lorenz · 2025
Mobile behavioural biometrics, leveraging touchscreen and background sensor data, offer a promising approach to Continuous Authentication (CA). However, the performance of these systems can vary significantly under different attack scenarios. This study evaluates the effectiveness of the regularisation technique in improving authentication accuracy within Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) architecture. Using the BehavePassDB dataset, we test four regularisation techniques (Ridge, Lasso, Bayesian, and ElsticNet) on accelerometer sensor data across various tasks, including Keystroke, Readtext, Gallery, and Tap. Results demonstrate that integrating regularisation techniques with LSTM-based models consistently outperforms the BBCA system, particularly in random and skilled attack scenarios, with Area Under the Curve (AUC) improvements of up to 15%. These findings underscore the potential of combining advanced neural networks with regularisation techniques to enhance mobile biometric systems.