Detecting abnormal user behavior through pattern-mining input device analytics

Abstract

This paper presents a method for detecting patterns in the usage of a computer mouse that can give insights into user's cognitive processes. We conducted a study using a computer version of the Memory game (also known as the Concentration game) that allowed some participants to reveal the content of the tiles, expecting their low-level mouse interaction patterns to deviate from those of normal players with no access to this information. We then trained models to detect these differences using task-independent input device features. The models detected cheating with 98.73% accuracy for players who cheated or did not cheat consistently for entire rounds of the game, and with 89.18% accuracy for cases in which players enabled and then disabled cheating within rounds.