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

arXiv: https://arxiv.org/abs/2307.11000 · HTML: https://mlsanity.com/paper/2307.11000/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2023.

## Built on by (most cited first, 5 of 5)

- [Temporal Encoding Strategies for Energy Time Series Prediction](https://mlsanity.com/paper/2503.15456/) (2025)
- [IEEE BigData 2023 Keystroke Verification Challenge (KVC)](https://mlsanity.com/paper/2401.16559/) (2024)
- [KVC-onGoing: Keystroke Verification Challenge](https://mlsanity.com/paper/2412.20530/) (2024)
- [ActReal: System-Level Mobile Agents Challenge Mobile Automation Detection](https://mlsanity.com/paper/2608.30038/) (2026)
- [Handwritten Digit Leakage from Smartphone Motion Sensors Across Unseen Users and Phone Models](https://mlsanity.com/paper/2609.32117/) (2026)

## Abstract (arXiv)

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.