Papers › BehaveFormer: A Framework with Spatio-Temporal Dual Attention Transformers for IMU...
BehaveFormer: A Framework with Spatio-Temporal Dual Attention Transformers for IMU enhanced Keystroke Dynamics
Dilshan Senerath, Sanuja Tharinda, Maduka Vishwajith, Sanka Rasnayaka, Sandareka Wickramanayake, Dulani Meedeniya
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections