GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters

TL;DR

This paper introduces GAN-Aimbot, a machine learning-based cheating method for first-person shooters that enhances player performance while evading detection, highlighting new security challenges in online gaming.

Contribution

The paper presents a novel GAN-based aimbot that improves cheating effectiveness and remains undetectable, demonstrating a significant security threat in multiplayer games.

Findings

GAN-Aimbot increases player accuracy and performance.

The method evades existing cheat detection systems.

It highlights the need for improved anti-cheat measures.

Abstract

Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by preventing cheating. Both traditional software-based methods and statistical systems have been successful in protecting against cheating, but recent advances in the automatic generation of content, such as images or speech, threaten the video game industry; they could be used to generate artificial gameplay indistinguishable from that of legitimate human players. To better understand this threat, we begin by reviewing the current state of multiplayer video game cheating, and then proceed to build a proof-of-concept method, GAN-Aimbot. By gathering data from various players in a first-person shooter game we show that the method improves players' performance while remaining hidden from automatic and manual protection mechanisms. By sharing this work we hope to raise awareness on this issue and encourage further research into protecting the gaming communities.