Student's CS2 thesis tracks players by mouse and keyboard habits

A student at the Norwegian University of Science and Technology has built his Master’s thesis around identifying Counter-Strike 2 players by how they use their mouse and keyboard, PC Gamer reported on Oct. 9. Christopher B. Didriksen shared findings on Reddit and said the thesis has been finished and graded A.

How the method works

Didriksen analyzed demos from competitive CS2 matches, PC Gamer reported. He said the separate mouse and keyboard signals give each player a “fingerprint” that stays stable “across maps, sessions, settings […] across matches played months apart, and across a sensitivity change from 800 to 640 eDPI.”

In a dataset of more than 1,000 players, mouse movement identified players “every time […] but often only by a fine margin,” Didriksen wrote. Keyboard use “picked out the right player 98% of the time.” PC Gamer reported that checking the two signals against each other makes the match to a real person highly accurate.

Aimed at smurfs and repeat offenders

Didriksen’s motivation was that a banned player can make a new account and be “back in a match the same evening.” He wrote that the method “found smurfs nobody had reported, and it is fast enough to check every new match against all of CS2’s monthly players.”

He proposes the system would sit alongside Valve Anti-Cheat and Trust Factor and answer a question they cannot: whether two accounts belong to the same human.

“The thesis is now finished and got an A, and none of it would have been possible without you.” – Christopher B. Didriksen, in a post to the r/CS2 subreddit

Limits and participants

Didriksen said the method is not a perfect fix. Shared accounts easily confuse the fingerprinting, PC Gamer reported.

Several Reddit users signed up earlier this year to take part in the research, and some revealed their own smurf accounts. Didriksen said this “turned out to be some of the most valuable data I had.”