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

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 is equipped with a well-defined workflow. Hawk is evaluated 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, higher recall and similar false positive rate compared to the in-use anti-cheat, and the ability to capture cheaters who evaded official inspections.