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Continuous Authentication (CA) using behavioural biometrics is a type ofbiometric identification that recognizes individuals based on their uniquebehavioural characteristics, like their typing style. However, the existingsystems that use keystroke or touch stroke data have limited accuracy andreliability. To improve this, smartphones' Inertial Measurement Unit (IMU)sensors, which include accelerometers, gyroscopes, and magnetometers, can beused to gather data on users' behavioural patterns, such as how they hold theirphones. Combining this IMU data with keystroke data can enhance the accuracy ofbehavioural biometrics-based CA. This paper proposes BehaveFormer, a newframework that employs keystroke and IMU data to create a reliable and accuratebehavioural biometric CA system. It includes two Spatio-Temporal Dual AttentionTransformer (STDAT), a novel transformer we introduce to extract morediscriminative features from keystroke dynamics. Experimental results on threepublicly available datasets (Aalto DB, HMOG DB, and HuMIdb) demonstrate thatBehaveFormer outperforms the state-of-the-art behavioural biometric-based CAsystems. For instance, on the HuMIdb dataset, BehaveFormer achieved an EER of2.95\%. Additionally, the proposed STDAT has been shown to improve theBehaveFormer system even when only keystroke data is used. For example, on theAalto DB dataset, BehaveFormer achieved an EER of 1.80\%. These resultsdemonstrate the effectiveness of the proposed STDAT and the incorporation ofIMU data for behavioural biometric authentication.

BehaveFormer: A Framework with Spatio-Temporal Dual Attention Transformers for IMU enhanced Keystroke Dynamics