Investigating the use of feature selection techniques for gender prediction systems based on keystroke dynamics

NASCIMENTO, Tuany Mariah Lima Do, OLIVEIRA, Andrelyne Vitoria Monteiro De, SANTANA, Laura Emmanuella Alves dos Santos and DA COSTA ABREU, Marjory (2021). Investigating the use of feature selection techniques for gender prediction systems based on keystroke dynamics. In: 11th International Conference on Pattern Recognition Systems (ICPRS 2021). IET, 115-120.

Preview

PDF Investigating__the_use_of_genetic_algorithm_for_feature_selection_in_keystrokedynamics_based_gender_prediction_systems.pdf - Accepted Version All rights reserved. Download (242kB) | Preview

Abstract

Biometric-based solutions keep expanding with new modalities, techniques and systems being proposed every so often. However, the first ones that were used for authentication, such as handwritten signature and keystroke dynamics, continue to be relevant in our digital world, despite their analogical origin. In special, keystroke dynamics has had an increase in popularity with the advent of social networks, making the need to continue to authenticate in desktop or game-based user verification more prevalent and this became an open door to risky situations such as paedophilia, sexual abuse, harassment among others. One of the ways to combat this type of crime is to be able to verify the legitimacy of the gender of the person the user is typing with. Despite the fact that keystroke dynamics is well accepted and reliable, this technique can have far too many attributes to be analysed which can lead to the use of redundant or irrelevant information. Therefore, propose a comparative study between two features selection approaches, hybrid (filter + wrapper) and wrapper. They will be tested by using a genetic algorithm, a particle swarm optimisation, a k -NN, a SVM, and a Naive Bayes as classifiers, as well as, the Correlation and Relief filters. From the results obtained, it can be said that the two proposed hybrid approaches reduce the number of attributes, without negatively impacting the accuracy of the classification, and being less costly than the traditional PSO.

Item Type:	Book Section
Identification Number:	https://doi.org/10.1049/icp.2021.1446
Page Range:	115-120
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	09 Sep 2021 09:52
Last Modified:	19 Oct 2021 10:06
URI:	https://shura.shu.ac.uk/id/eprint/28996

Actions (login required)

View Item

Downloads

Publication Metrics