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Kaformer: efficient transformer for keystroke authentication
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AI
DOI：10.1007/s40747-026-02385-2.png)
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Abstract
En 中文
Keystroke dynamics-based authentication leverages an individual’s unique typing behavior for identity verification. However, existing keystroke authentication techniques face two critical challenges: limited recognition accuracy and substantial computational overhead. To address these issues, we propose KAformer, an efficient Transformer architecture for keystroke authentication. Specifically, we introduce a Multi-head Temporal Depthwise Convolutional Attention (MTDCA) mechanism and a Gated Feed-Forward Network (GFFN) to replace the conventional multi-head self-attention and feed-forward network, respectively. These two modules are functionally complementary and tightly coupled. Experimental results on the Aalto desktop and mobile datasets demonstrate that KAformer achieves 99.2% AUC with 0.56% EER, and 99.12% AUC with 0.62% EER, respectively. Furthermore, compared with the standard Transformer architecture, KAformer reduces computational complexity (FLOPs) and the number of parameters by nearly 50%, thereby enhancing operational efficiency while simultaneously reducing computational resource requirements.
Keywords:
Behavioral biometrics
Keystroke dynamics
Authentication
Neural networks
Journal
C
IF:
4.6
Papers:
262
Citations:
0