User identification by keystroke dynamics using improved binary particle swarm optimisation

摘要

As a kind of behavioural characteristic, keystroke features are crucial to the accuracy of user identification system using shallow machine learning algorithms. Filter and wrapper feature selection algorithms are the two most important methods. The information gain and particle swarm optimisation algorithm represent the two feature optimisation methods, respectively. In this paper, new hybrid binary particle swarm optimisation methods combined with information gain theory are proposed in association with opposite-based learning and distributed techniques. The converted information gain values act as weight coefficients to adaptively adjust the flight speed of particles. The support vector machine (SVM) algorithm is applied to evaluate the performance of feature optimisation in terms of user identification accuracy and feature reduction rate. Experimental results of three public keystroke datasets show that the proposed optimisation methods achieve better classification accuracy with fewer features than four existing optimisation methods.