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

TL;DR

This paper introduces HAWK, a server-side anti-cheat framework for FPS games like CS:GO, using machine learning to mimic human expert detection, leveraging multi-view features, and evaluated on real-world datasets showing improved efficiency and cheat detection.

Contribution

The paper presents HAWK, a novel server-side anti-cheat system that employs machine learning and multi-view features to improve cheat detection in FPS games, addressing limitations of existing solutions.

Findings

HAWK achieves shorter ban times than existing anti-cheats.

It significantly reduces manual review efforts.

It detects cheats that bypass official inspections.

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.