Personal identification based on mobile-based keystroke dynamics

Tharwat, A, Ibrahim, A, Gaber, T ORCID: https://orcid.org/0000-0003-4065-4191 and Hassanien, AE 2018, Personal identification based on mobile-based keystroke dynamics , in: International Conference on Advanced Intelligent Systems and Informatics.

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Abstract

This paper is addressing the personal identification problem by using mobile-based keystroke dynamics of touch mobile phone. The proposed approach consists of two main phases, namely feature selection and classification. The most important features are selected using Genetic Algorithm (GA). Moreover, Bagging classifier used the selected features to identify persons by matching the features of the unknown person with the labeled features. The outputs of all Bagging classifiers are fused to determine the final decision. In this experiment, a keystroke dynamics database for touch mobile phones is used. The database, which consists of four sets of features, is collected from 51 individuals and consists of 985 samples collected from males and females with different ages. The results of the proposed model conclude that the third subset of features achieved the best accuracy while the second subset achieved the worst accuracy. Moreover, the fusion of all classifiers of all ensembles will improve the accuracy and achieved results better than the individual classifiers and individual ensembles.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Advances in Intelligent Systems and Computing
Publisher: Springer
Related URLs:
Depositing User: Dr. Tarek Gaber
Date Deposited: 11 Sep 2019 10:52
Last Modified: 11 Sep 2019 10:52
URI: http://usir.salford.ac.uk/id/eprint/52065

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