Free-text Keystroke Authentication using Transformers: A Comparative Study of Architectures and Loss Functions. (arXiv:2310.11640v1 [cs.CR])

Keystroke biometrics is a promising approach for user identification and
verification, leveraging the unique patterns in individuals' typing behavior.
In this paper, we propose a Transformer-based network that employs
self-attention to extract informative features from keystroke sequences,
surpassing the performance of traditional Recurrent Neural Networks. We explore
two distinct architectures, namely bi-encoder and cross-encoder, and compare
their effectiveness in keystroke authentication. Furthermore, we investigate
different loss functions, including triplet, batch-all triplet, and WDCL loss,
along with various distance metrics such …

attention authentication biometrics extract features free functions identification informative loss network networks neural networks patterns performance study text transformers verification