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Keystroke dynamics-based user authentication (KDA) based on long and freelytyped text is an enhanced user authentication method that can not only identifythe validity of current users during login but also continuously monitors theconsistency of typing behavior after the login process. Previous long andfreely typed text-based KDA methods had difficulty incorporating the keysequence information and handling variable lengths of keystrokes, which in turnresulted in lower authentication performance compared to KDA methods based onshort and fixed-length text. To overcome these limitations, we propose arecurrent neural network (RNN)-based KDA model. As the RNN model can process anarbitrary length of input and target sequences, our proposed model takes twoconsecutive keys as the input sequence and actual typing time for thecorresponding key sequence as the target sequence. Based on experimentalresults involving 120 participants, our proposed RNN-KDA model yielded the bestauthentication performance for all training and test length combinations interms of equal error rate (EER). It achieved a 5%-6% EER using only 10 testkeystrokes while the EERs of other benchmark methods were above 20%. Inaddition, its performance steadily and more rapidly improves compared to thebenchmark methods when the length of training keystrokes increases.

Recurrent neural network-based user authentication for freely typed keystroke data