Machine Learning Aimbot Detection in Call of Duty

As online gaming grows, maintaining fair play is increasingly difficult. Call of Duty, a highly competitive first-person shooter, faces a surge in aimbot usage—cheats that enable near-perfect accuracy, undermining ranked play. Traditional detection methods are ineffective against advanced cheats that mimic human behavior.

Machine learning presents a scalable and adaptive solution. We developed a data pipeline that collects features such as angle velocity, acceleration, etc. to train a deep neural network and deployed it. We are processing 30 million rows of data per hour for this detection on Databricks Platform.

As cheat developers evolve, so must detection techniques. This session will explore our methodologies, challenges and future directions, demonstrating how machine learning is transforming anti-cheat strategies and preserving competitive integrity in online gaming and how Databricks enabling us to do so.