Keystroke Dynamics Classification Based On LSTM and BLSTM Models

By adopting keystroke dynamics, authentication applications can integrate advanced identity proofing technology for detecting fraud and prevent unauthorized access. However, understanding a user's keystroke dynamics behavior in real applications is a challenging task regarding that this behavior is notably changing over time. To mitigate this problem, we apply, in this paper, the long short-term memory (LSTM) model that recognizes a continuous sequences of keystroke dynamics to identify users of public datasets. We also consider the bidirectional long short-term memory (BLSTM) as it maintain information about the future data. Hence, collecting information about intra-class variations of the keystroke dynamics from both past and future data, is an interesting solution to our problem. The obtained results are promising since we obtained an accuracy rate over than 60% for both architectures when dealing with public databases.