Researcher Proposes Banning Cheaters By Keyboard And Mouse Habits
The proposal targets the account, not the input pattern behind it, and an input-based fingerprint is one of the few enforcement signals that survives a new account, which is why the paper's limits on data and accuracy will decide whether it is usable at scale.
Reporting from 1 source: Automaton.
A Reddit user named Magga, described as a student at the Norwegian University of Science and Technology, has outlined a master's thesis method for identifying whether game accounts belong to the same person through keyboard and mouse input habits. Using Counter-Strike 2 replays, the research builds a player fingerprint from input signals. Mouse fingerprints identified the correct player in every experiment, keyboard fingerprints at a 98 percent rate.
The pitch rests on a claim that input habits are hard to change. Thousands of repeated operations in Counter-Strike 2 imprint movement as muscle memory, Magga argues, which makes deliberate alteration difficult. The procedure pulls mouse and keyboard signals from replay data, converts them into a player fingerprint, then scores similarity between fingerprints from different matches to judge whether the same person is playing.
Magga reports fingerprints held up across matches months apart and after a mouse sensitivity change. Experiments returned correct identification every time from mouse input and 98 percent of the time from keyboard input, with a correlation of 0.11 between the two sets, meaning a coincidental match in one is unlikely to repeat in the other.
Synthesized by Yomimono from the 1 cited source below, including Japanese-language reporting where cited, then editorially reviewed before publishing.