Title: A deep-learning approach to game bot identification via behavioural features analysis in complex massively-cooperative environments

Authors: Alfredo Cuzzocrea; Fabio Martinelli; Francesco Mercaldo

Addresses: iDEA Lab, University of Calabria, Rende, Italy; LORIA, Nancy, France ' Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy ' Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy

Abstract: In the so-called massively multiplayer online role-playing games (MMORPGs), malicious players have the possibility of obtaining some kind of gains from competitions, via easy victories achieved thanks to the introduction of game bots in the games. In order to maintain fairness among players, it is important to detect the presence of game bots during video games so that they can be expelled from the games. This paper describes an approach to distinguish human players from game bots based on behavioural analysis. This implemented via supervised machine learning (ML) and deep learning (DL) algorithms. In order to detect game bots, considered algorithms are first trained with labelled features and then used to classify unseen-before features. In this paper, the performance of our game bots detection approach is experimentally obtained. The dataset we use for training and classification is extracted from logs generated during online video games matches of a real-life MMORPG.

Keywords: game bot detection; complex massively-cooperative environments; machine learning; deep learning; massively multiplayer online role-playing games; MMORPGs.

DOI: 10.1504/IJDMMM.2023.129963

International Journal of Data Mining, Modelling and Management, 2023 Vol.15 No.1, pp.1 - 29

Received: 28 Sep 2020
Accepted: 11 Jun 2021
Published online: 04 Apr 2023 *