# Identify As A Human Does: A Pathfinder of Next-Generation Anti-Cheat Framework for First-Person Shooter Games

arXiv: https://arxiv.org/abs/2409.14830 · HTML: https://mlsanity.com/paper/2409.14830/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 84 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters](https://mlsanity.com/paper/2205.07060/) (2022)
- [Aim Low, Shoot High: Evading Aimbot Detectors by Mimicking User Behavior](https://mlsanity.com/paper/2004.12183/) (2020)
- [CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System](https://mlsanity.com/paper/2406.19720/) (2024)

## Built on by (most cited first, 2 of 2)

- [Adversarial Reinforcement Learning Framework for ESP Cheater Simulation](https://mlsanity.com/paper/2509.24274/) (2025)
- [XGuardian: Towards Explainable and Generalized AI Anti-Cheat on FPS Games](https://mlsanity.com/paper/2601.18068/) (2026)

## Abstract (arXiv)

The gaming industry has experienced substantial growth, but cheating in online games poses a significant threat to the integrity of the gaming experience. Cheating, particularly in first-person shooter (FPS) games, can lead to substantial losses for the game industry. Existing anti-cheat solutions have limitations, such as client-side hardware constraints, security risks, server-side unreliable methods, and both-sides suffer from a lack of comprehensive real-world datasets. To address these limitations, the paper proposes HAWK, a server-side FPS anti-cheat framework for the popular game CS:GO. HAWK utilizes machine learning techniques to mimic human experts' identification process, leverages novel multi-view features, and it is equipped with a well-defined workflow. The authors evaluate HAWK with the first large and real-world datasets containing multiple cheat types and cheating sophistication, and it exhibits promising efficiency and acceptable overheads, shorter ban times compared to the in-use anti-cheat, a significant reduction in manual labor, and the ability to capture cheaters who evaded official inspections.