Researcher develops method to fingerprint Counter-Strike players using mouse and keyboard input

Player Fingerprinting Technique Developed

A master's student from the Norwegian University of Science and Technology created a fingerprinting technique to identify gamers based on their mouse and keyboard input patterns. The researcher, u/Magga_, shared the study's findings on the r/GlobalOffensive subreddit, detailing how they tracked input signals from over a thousand players and correlated them.

High Accuracy in Player Identification

The technique achieved 100% accuracy in identifying unique players from mouse data and 98% accuracy from keyboard data. Combining both metrics significantly increases detection accuracy. While concerns exist about unrelated players having similar styles, the statistical probability of two individuals having similar mouse and keyboard habits is low, with a correlation of 0.11 between stranger's mouse and keyboard habits.

Detecting Linked Accounts

During testing, the method initially showed 'false positives,' which upon investigation, revealed previously unknown account pairs and even linked four different accounts to a single person. This indicates the system's ability to uncover connections between accounts that might otherwise go unnoticed.

Operational System, Not Just a Concept

The researcher states this is a working system, not merely a proof of concept. It is designed to continuously process game demos and link accounts, building a reference system with each uploaded match. The system can operate on a single 20GB slice of an Nvidia A100 GPU, despite the large volume of data it handles.

Limitations and Future Scope

The researcher noted that the amount of data tested is relatively thin and expressed a desire to see the system applied to the full population size of CS2 players. A limitation identified is that shared accounts would compromise the system's effectiveness.