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A master’s student from the Norwegian University of Science and Technology developed a technique to identify gamers by analyzing their mouse and keyboard input patterns. The researcher, u/Magga_, tracked signals from over 1,000 players on the r/GlobalOffensive subreddit to correlate movements between devices. This method allows for unique user fingerprinting and matching specific accounts, including those used as smurf accounts. According to u/Magga_, the mouse dataset identified unique players 100% of the time, while keyboard fingerprinting achieved a 98% accuracy rate. Combining these two metrics creates highly accurate detection because they measure entirely different movements.

The study found that while unrelated people might share similar play styles, the statistical probability of two strangers having identical mouse and keyboard habits is very low. u/Magga_ noted that the correlation between how alike their mouse habits are and how alike their keyboard habits are is just 0.11, where 0 means unrelated and 1 means they always go together. Initially, the method returned false positives, but deeper investigation revealed these accounts were actually related to each other, sometimes connecting four different accounts to one person. The system is designed to process data on a single 20GB Nvidia A100 GPU and aims to build a reference system with every uploaded match.

Although u/Magga_ claims this is a working system rather than just a proof of concept, he acknowledged limitations such as shared accounts breaking the detection. He also emphasized that policy choices regarding bans are separate from the technical limits of the method. The researcher suggested that cheating once should not bar someone from a game for life and recommended that bans expire after a set period. While this technology could help developers prevent cheaters from ruining the online experience, it raises significant issues regarding privacy and ethical use in gaming.