Free-text keystroke continuous authentication using CNN and RNN

doi:10.16511/j.cnki.qhdxxb.2018.26.048

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Abstract Personal keystroke input patterns are difficult to imitate and can be used for identity authentication. The personal keystroke input data for a free-text can be used to learn the unique keystroke mode of a person. Detection based on a user's free-text keystrokes can be used for continuous identity authentication without affecting the user input. This paper presents a model that divides the keystroke data into fixed-length keystroke sequences and converts the keystroke time data in the keystroke sequence into a keystroke vector according to the time characteristics of the keystrokes. A convolutional neural network and a recurrent neural network are then used to learn the sequences of the personal keystroke vectors for identity authentication. The model was tested on an open data set with an optimal false rejection rate (FRR) of 1.95%, a false acceptance rate (FAR) of 4.12%, and an equal error rate (EER) of 3.04%.

Keywords authentication keystroke dynamics free-text convolutional neural networks recurrent neural networks

Issue Date: 13 December 2018

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	LU Xiaofeng
	ZHANG Shengfei
	YI Shengwei

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LU Xiaofeng,ZHANG Shengfei,YI Shengwei. Free-text keystroke continuous authentication using CNN and RNN[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(12): 1072-1078.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.26.048 OR http://jst.tsinghuajournals.com/EN/Y2018/V58/I12/1072

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