Software that automatically aims at targets, locks onto enemies, or fires weapons.
Supporting Academic Articles
"Is It Legit, To You?". An Exploration of Players' Perceptions of Cheating in a Multiplayer Video Game: Making Sense of Uncertainty
Arianna Boldi, Amon Rapp
International Journal of Human–Computer Interaction, 2024, Vol. 40, No. 15, 4021-4041
Target: Third-party software cheats (wallhacks, aimbots, aimlock), game glitch exploitation, technological advantages (VPNs, expensive hardware)
Aim: Boldi, A., & Rapp, A. (2024). "Is It Legit, To You?". An Exploration of Players' Perceptions of Cheating in a Multiplayer Video Game: Making Sense of Uncertainty. International Journal of Human–Computer Interaction, 40(15), 4021-4041.
Recommendation: (Boldi & Rapp, 2024)
Cheating in E-Sports: A Proposal to Regulate the Growing Problem of E-Doping
Jamie Hwang
Northwestern University Law Review, Vol. 116, No. 5, 2022
Target: E-doping (software hacks, cheats, digital doping, mechanical doping)
Aim: Hwang, J. (2022). Cheating in E-Sports: A proposal to regulate the growing problem of e-doping. Northwestern University Law Review, 116(5), 1283-1318.
Recommendation: (Hwang, 2022)
GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters
Anssi Kanervisto and Tomi Kinnunen and Ville Hautamäki
IEEE Transactions on Games Vol. 15 No. 4 December 2023
Target: Machine learning-based aimbots using Generative Adversarial Networks (GANs), human-like cheating behavior generation
Aim: Kanervisto, A., Kinnunen, T., & Hautamäki, V. (2023). GAN-Aimbots: Using machine learning for cheating in first person shooters. IEEE Transactions on Games, 15(4), 566-579.
Recommendation: (Kanervisto et al., 2023)
Redefining the risks of kernel-level anti-cheat in online gaming
Anton Maario, Vinod Kumar Shukla, A. Ambikapathy, Purushottam Sharma
IEEE SPIN Conference 2021
Target: Multiple cheat types: aimbot, triggerbot, wallhack, ESP, mobility hacks, hardware cheats
Aim: Maario, A., Shukla, V. K., Ambikapathy, A., & Sharma, P. (2021). Redefining the risks of kernel-level anti-cheat in online gaming. In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 676-680).
Recommendation: (Maario et al., 2021)
Detecting Cheating in Computer Games using Data Mining Methods
Alexandre Philbert
American Journal of Computer Science and Information Technology, Vol. 6, No. 3, 2018
Target: Signature-based cheats, metamorphic/polymorphic cheats, malicious executables, aimbot, wall hack, no recoil, radar hack
Aim: Philbert, A. (2018). Detecting cheating in computer games using data mining methods. American Journal of Computer Science and Information Technology, 6(3), 26.
Recommendation: (Philbert, 2018)
Deep learning and multivariate time series for cheat detection in video games
José Pedro Pinto, André Pimenta, Paulo Novais
Machine Learning Journal, Vol. 110, pp. 3037-3057, 2021
Target: Triggerbot and aimbot cheats, human-computer interaction data manipulation, keystroke and mouse movement analysis
Aim: Pinto, J. P., Pimenta, A., & Novais, P. (2021). Deep learning and multivariate time series for cheat detection in video games. Machine Learning, 110(11), 3037-3057.
Recommendation: (Pinto et al., 2021)
A statistical aimbot detection method for online FPS games
Su-Yang Yu, Nils Hammerla, Jeff Yan, Peter Andras
2012 International Joint Conference on Neural Networks (IJCNN)
Target: Aiming robot (aimbot) - automatic target acquisition and retention tools
Aim: Yu, S. Y., Hammerla, N., Yan, J., & Andras, P. (2012). A statistical aimbot detection method for online FPS games. In 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Recommendation: (Yu et al., 2012)