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

Dilshan Senarath, Sanuja Tharinda, Maduka Vishvajith, Sanka Rasnayaka, Sandareka Wickramanayake, Dulani Meedeniya

Abstract: 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 using 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 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 Transformers (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, BehaveFormer achieved an EER of 2.95% on the HuMIdb. Additionally, the proposed STDAT has been shown to improve the BehaveFormer system even when only keystroke data is used. For example, BehaveFormer achieved an EER of 1.80%. The code is available at https://github.com/DilshanSenarath/BehaveFormer.

External IDs:dblp:conf/icb/SenarathTVRWM23