FKTAN: Fusion Keystroke Time-Textual Attention Networks for Continuous Authentication

With the rapid development of computer technology, the traditional Internet data security and information privacy issues are gradually expanding to all aspects of society as a whole. As the first line of defense for information security, identity authentication technology becomes crucial. Among the many authentication technologies, continuous authentication technology has gained increasing attention. In this paper, we design fusion keystroke time-textual attention networks for continuous authentication based on the keystroke data (keystroke time series, keystroke text) when users enter free-text. Specifically, the corresponding keystroke time series and the corresponding keystroke text are first obtained based on the original keystroke data, and then the keystroke time series and the keystroke text are input into the BiLSTM model and the pre-training model, respectively; the BiLSTM can better capture the temporal features, and the pre-training model can better capture the textual features when authenticating the user. Finally, the two information are fed into the cross attention model to better integrate the two information. Experiments show that the FKTAN model achieves promising results on two datasets, Clarkson II keystroke dataset and Buffalo dataset, outperforming all baseline models.