Title: User identification by keystroke dynamics using improved binary particle swarm optimisation

Authors: Tong Wu; Kangfeng Zheng; Guangzhi Xu; Chunhua Wu; Xiujuan Wang

Addresses: School of Cyberspace Security, Beijing University of Posts and Telecommunications, 100876, Beijing, China ' School of Cyberspace Security, Beijing University of Posts and Telecommunications, 100876, Beijing, China ' School of Automation, Beijing University of Posts and Telecommunications, 100876, Beijing, China ' School of Cyberspace Security, Beijing University of Posts and Telecommunications, 100876, Beijing, China ' Faculty of Information Technology, Beijing University of Technology, 100124, Beijing, China

Abstract: 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.

Keywords: binary particle swarm optimisation; BPSO; information gain; opposite-based learning; feature optimisation; user identification; keystroke dynamics; support vector machine; SVM.

DOI: 10.1504/IJBIC.2019.103613

International Journal of Bio-Inspired Computation, 2019 Vol.14 No.3, pp.171 - 180

Received: 18 Sep 2018
Accepted: 31 May 2019

Published online: 13 Nov 2019 *

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