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

TL;DR

This work tackles cheating in online FPS games by introducing Hawk, a server-side anti-cheat framework for CS:GO that mimics human expert detection using multi-view features derived from replay data. Hawk decomposes the problem into three complementary perspectives—POV analysis (RevPov), statistical review (RevStats), and sense–performance consistency (ExSPC)—and integrates their outputs through a dynamic multi-view fusion network (Mvin) with a configurable Task-Specified Threshold Optimizer (TSTO). The approach is evaluated on large, real-world CS:GO datasets comprising 2,979 aimbots and 2,971 wallhacks across 56,041 players, achieving recall up to 84% and accuracy around 80%, while substantially reducing manual labor and shortening ban cycles compared to official inspections. Key contributions include a novel four-subsystem workflow, real-world data validation, open-source Hawk and datasets, and demonstrated robustness to cheat evolution, making Hawk a practical pathfinder for next-generation FPS anti-cheat deployments.

Abstract

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.

Figures (8)

• Figure 1: Cyclical workflow of Hawk.
• Figure 2: Box plots of comparative visualization between honest players and cheaters on top-12 structured features under the Mann-Whitney U test with distinction descending order.
• Figure 3: Comparative behavioral visualization between honest players and cheaters with respect to time scales.
• Figure 4: Hawk framework illustration. (a)-RevPov, (b)-RevStats, (c)-ExSPC are corresponding to three observations in \ref{['sec: Fact OBS']}, (d)-Mvin is an integration network for combining the determinations from the aforementioned three subsystems.
• Figure 5: Experiments' process and labeling details.