Handedness Recognition through Keystroke-Typing Behavior in Computer Forensics Analysis

Abstract

Recognizing computer users' handedness provides important clues for profiling computer criminals in digital forensic analysis. Existing technologies for handedness recognition have two main problems that limit their applicability in the scenario of digital crimes: they can be intrusive, and they require costly equipment. Our solution is to infer users' handedness by analyzing keystroke-typing behavior. Field studies are first conducted to gather users' keystroke-typing data during their interaction with computers. Timing features are extracted to characterize users' typing rhythms, and the correlation between keystroke features and handedness is analyzed. Classification techniques are then developed for handedness recognition. Experimental results show that the handedness could be efficiently and accurately inferred from users' keystroke-typing behavior, with recognition rates expressed by the Area Under the ROC Curve (AUC) of 87.75%. To our knowledge, this is the first work that infers users' handedness based on their keystroke-typing biometric during interaction with computers, without dedicated and explicit actions that require attention from users.