Student's Mouse-and-Keyboard Fingerprinting IDs CS2 Smurfs
A Norwegian University of Science and Technology master's student shared a technique that links Counter-Strike accounts, including smurfs, by input patterns.
A master's student at the Norwegian University of Science and Technology has publicly shared a fingerprinting technique that identifies Counter-Strike players by their mouse and keyboard input patterns and links their alternate accounts, including smurfs. The results were posted on the r/GlobalOffensive subreddit by u/Magga_, according to tomshardware.com.
The study tracked and correlated mouse and keyboard signals from more than a thousand players. In testing, the mouse dataset identified unique players 100% of the time, while keyboard fingerprinting was correct 98% of the time. The researcher said the correlation between strangers' mouse and keyboard habit similarity is 0.11.
The system is designed to ingest demos and continuously link accounts, building a reference system with every uploaded match. It is built to run on a single 20GB slice of an Nvidia A100 GPU.
Limitations and scope
The researcher said the amount of data tested was thin and wants the approach tested at the real CS2 population size. A noted limitation is that shared accounts would break the system.
In earlier background, Riot Games dropped its always-on anti-cheat requirement for Valorant after player criticism, while Valorant soft-bricked $6,000 cheating hardware. Counter-Strike 2 still has over 1.2 million players at peak.
Quick answers
How accurate is the mouse-and-keyboard fingerprinting technique?
The mouse dataset identified unique players 100% of the time, while keyboard fingerprinting was correct 98% of the time, according to the researcher's results.
What hardware does the system need?
It is designed to run on a single 20GB slice of an Nvidia A100 GPU.
What is a known limitation of the system?
Shared accounts would break the system, the researcher noted.