A student at the Norwegian University of Science and Technology has proposed a method to identify repeat offenders in Counter-Strike 2 by analyzing how they move their mouse and keyboard. Christopher B. Didriksen based his Master’s thesis on this biometric approach, which aims to solve the persistent problem of banned players creating new accounts to continue cheating.
Mouse and Keyboard Fingerprints
Didriksen analyzed competitive match demos to extract unique movement patterns from players. The research found that these patterns remain stable across different maps, game sessions, and even when sensitivity settings change. For example, a player’s input style stayed consistent whether they adjusted their effective dots per inch (eDPI) from 800 to 640.
In a dataset containing over 1,000 players, the system correctly identified individuals every time using mouse movement data. Keyboard input alone identified the correct player 98% of the time. When researchers combined both metrics, the accuracy of cross-referencing the data increased significantly. Didriksen noted that this method was fast enough to check every new match against the entire monthly player base.
Targeting Smurf Accounts
The primary motivation for this research was the ease with which banned cheaters can return to the game. Existing systems like Valve Anti-Cheat and Trust Factor struggle to determine if a new account belongs to a previously banned human. Didriksen’s system addresses this gap by answering the specific question of whether the current player is the same person as a past offender.
The study also identified “smurf” accounts, which are secondary profiles used by experienced or high-ranking players to play against lower-skilled opponents. Didriksen stated that the biometric analysis found smurf accounts that had not been reported by other players. This capability offers a new layer of detection that complements existing anti-cheat measures.
However, the researcher acknowledged limitations in the system. The method does not account for players who genuinely change their behavior or stop cheating on a fresh account. Additionally, shared accounts can confuse the biometric data, as multiple people using the same profile would generate inconsistent movement patterns.
Community Data and Thesis Results
Didriksen relied on data provided by volunteers from the r/CS2 subreddit earlier this year. Some participants revealed their smurf accounts, which the researcher described as some of the most valuable data collected for the study. The thesis was completed with a grade of A, and Didriksen credited the community for their participation.
The research highlights a technical approach to a social problem in online gaming. By focusing on human input patterns rather than just software signatures, the system offers a potential path to reducing the impact of repeat offenders in competitive matches.
Source: PC Gamer
