Master's Thesis Uses Mouse Movement to Fingerprint CS2 Cheaters and Smurfs
A Norwegian University of Science and Technology student says mouse biometrics identified players correctly in every case across a dataset of over 1,000 Counter-Strike 2 players.
Christopher B. Didriksen, a student at the Norwegian University of Science and Technology, has completed a Master's thesis proposing a biometric method to identify cheaters and smurf accounts in Counter-Strike 2, according to pcgamer.com. The thesis proposes identifying players by a movement "fingerprint" derived from their mouse and keyboard use.
The research analysed demos from competitive CS2 matches. In a dataset of over 1,000 players, mouse movement biometrics identified players correctly every time, while keyboard use was correct 98% of the time.
Didriksen said the method found smurfs that nobody had reported and is fast enough to check every new match against all of CS2's monthly players. He proposed the system to work alongside Valve Anti-Cheat and Trust Factor. The thesis received a grade of A.
Data from volunteer players
Earlier this year, a number of Redditors signed up to participate in Didriksen's research, with some revealing their smurf accounts, which Didriksen called "some of the most valuable data I had".
Quick answers
What did the thesis propose for Counter-Strike 2?
It proposed identifying players by a movement "fingerprint" derived from their mouse and keyboard use, to detect cheaters and smurf accounts.
How accurate was the mouse movement method?
In a dataset of over 1,000 players, mouse movement biometrics identified players correctly every time, while keyboard use was correct 98% of the time.
How would the system fit with existing CS2 anti-cheat?
It was proposed to work alongside Valve Anti-Cheat and Trust Factor, and Didriksen said it is fast enough to check every new match against all of CS2's monthly players.