  Continuous Authentication (CA) using behavioural biometrics is a type of
biometric identification that recognizes individuals based on their unique
behavioural characteristics, like their typing style. However, the existing
systems that use keystroke or touch stroke data have limited accuracy and
reliability. To improve this, smartphones' Inertial Measurement Unit (IMU)
sensors, which include accelerometers, gyroscopes, and magnetometers, can be
used to gather data on users' behavioural patterns, such as how they hold their
phones. Combining this IMU data with keystroke data can enhance the accuracy of
behavioural biometrics-based CA. This paper proposes BehaveFormer, a new
framework that employs keystroke and IMU data to create a reliable and accurate
behavioural biometric CA system. It includes two Spatio-Temporal Dual Attention
Transformer (STDAT), a novel transformer we introduce to extract more
discriminative features from keystroke dynamics. Experimental results on three
publicly available datasets (Aalto DB, HMOG DB, and HuMIdb) demonstrate that
BehaveFormer outperforms the state-of-the-art behavioural biometric-based CA
systems. For instance, on the HuMIdb dataset, BehaveFormer achieved an EER of
2.95\%. Additionally, the proposed STDAT has been shown to improve the
BehaveFormer system even when only keystroke data is used. For example, on the
Aalto DB dataset, BehaveFormer achieved an EER of 1.80\%. These results
demonstrate the effectiveness of the proposed STDAT and the incorporation of
IMU data for behavioural biometric authentication.
