An empirical biometric-based study for user identification with different neural networks in the online game League of Legends

DA SILVA, V R and DA COSTA ABREU, Marjory (2018). An empirical biometric-based study for user identification with different neural networks in the online game League of Legends. In: 2018 International Joint Conference on Neural Networks (IJCNN) 2018 proceedings. IEEE.

Preview

PDF PID5328503.pdf - Accepted Version All rights reserved. Download (115kB) | Preview

Abstract

The popularity of computer games has grown exponentially in the last years. Although such games were created to promote competition and promote self-improvement, there are some recurrent issues. One that has received the least amount of attention so far is the problem of 'account sharing' which is when a player shares his/her account with more experienced players to make progress in the game. The companies running those games tend to punish this behaviour, but this specific case is hard to identify. Since, the popularity of neural networks has never been higher, the aim of this study is to investigate how different neural network algorithms behave when analysing a database of biometric information (keystroke and mouse dynamics) regarding the game League of Legends, and how those algorithms are affected by how frequently a sample is collected.

Item Type:	Book Section
Additional Information:	Electronic ISSN: 2161-4407 © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identification Number:	https://doi.org/10.1109/IJCNN.2018.8489164
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	13 Dec 2019 13:08
Last Modified:	18 Mar 2021 02:35
URI:	https://shura.shu.ac.uk/id/eprint/25393

Actions (login required)

View Item

Downloads

Publication Metrics