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Three-factor authentication is the best available option for cybersecurity enhancement, which includes ‘know’ (password), ‘have’ (username, token, or card) and ‘are’ (biometrics). Each one makes this process stronger and more secure. Imagine an international team working on a long-term project by remotely logging into a secured server. In such a context, adding keystroke biometrics to authentication will definitely improves the cybersecurity. We propose to develop a set of recurrent neural network (RNN) models of utilizing keystroke dynamics as ones’ biometrics to enhance cybersecurity. Keystroke dynamics refers to the process of measuring and assessing human’s typing rhythm on digital devices. Keystroke timing information such as di-graph, dwell time and flight time are used in our experimental datasets. We propose to apply support vector machine and recurrent neural network to keystroke dynamics. Experimental results show the proposed methods are promising in contrast with traditional methods like nearest neighbor and Manhattan distance.
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