Continuous authentication by free-text keystroke based on CNN plus RNN

Abstract

Personal keystroke mode is difficult to imitate and can therefore be used for identity authentication. According to the keystroke data when a person inputs free text, the keystroke habit of the person can be learned. Detecting a user’s keystroke habits as the user enters text can continuously verify the user’s identity without affecting user input. This paper proposes to divide the user keystroke data into a fixed-length keystroke sequence, and convert the keystroke sequence into a keystroke vector sequence according to the time feature of the keystroke. A model of a recursive neural network plus a convolutional neural network is used to learn a sequence of individual keystroke vectors to obtain individual keystroke features for identity authentication. The model was tested using an open data set and the best False Rejection Rate(FRR) was 1.95%, False Acceptance Rate (FAR) was 4.12% and Equal Error Rate(EER) was 3.04%.