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User Identification by Keystroke Dynamics Based on Feature Correlation Analysis and Feature optimization

As a kind of behavioral characteristics, the analysis of keystroke behavior and the selection of keystroke features are crucial operations to improve the accuracy of user identification using shallow machine learning algorithms. In this paper, we discuss the typing behavior phenomena and put forward a targeted feature optimization strategy in order to meet the need of improving the accuracy of user identification. Three types of keystroke features are analyzed including duration time features (Hold Time) and two types of latency time features (UD Time and DD Time) from three aspects of features distribution, features correlation and features contribution. The differential evolution (DE) algorithm is used to optimize keystroke features and the new proposed fitness function of DE algorithm is defined based on the analysis of keystroke features. Finally, random forest (RF) algorithm is devoted to evaluate the performance of feature optimization. Feature analysis results show that latency time features distribution among users is more diverse than duration time; the two types of latency time features have a strong correlation of which is as high as 0.9766; the combination of duration and latency time features have the best classification accuracy. Final experimental results of an open dataset show that DE algorithm based on features correlation analysis selects features which have better contribution to user classification, reduces the correlation between UD Time and DD Time features, and improves the classification accuracy by an average of 2.6206%.