BotScreen: Trust Everybody, but Cut the Aimbots Yourself


Minyeop Choi, KAIST; Gihyuk Ko, Cyber Security Research Center at KAIST and Carnegie Mellon University; Sang Kil Cha, KAIST and Cyber Security Research Center at KAIST

Distinguished Paper Award Winner


Aimbots, which assist players to kill opponents in FirstPerson Shooter (FPS) games, pose a significant threat to the game industry. Although there has been significant research effort to automatically detect aimbots, existing works suffer from either high server-side overhead or low detection accuracy. In this paper, we present a novel aimbot detection design and implementation that we refer to as BotScreen, which is a client-side aimbot detection solution for a popular FPS game, Counter-Strike: Global Offensive (CS:GO). BotScreen is the first in detecting aimbots in a distributed fashion, thereby minimizing the server-side overhead. It also leverages a novel deep learning model to precisely detect abnormal behaviors caused by using aimbots. We demonstrate the effectiveness of BotScreen in terms of both accuracy and performance on CS:GO. We make our tool as well as our dataset publicly available to support open science.

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@inproceedings {291120,
author = {Minyeop Choi and Gihyuk Ko and Sang Kil Cha},
title = {{BotScreen}: Trust Everybody, but Cut the Aimbots Yourself},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
year = {2023},
isbn = {978-1-939133-37-3},
address = {Anaheim, CA},
pages = {481--498},
url = {},
publisher = {USENIX Association},
month = aug

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