Are you Telling the Truth? Detection of Identity Theft through Human Motor Control in the Metaverse

A user's digital identity is central to accessing and interacting within the metaverse, making it particularly vulnerable to abuse, such as identity theft. Fraudsters can operate anonymously, not only under a false name but also with a different appearance. Identifying users' true identities is challenging because traditional physical identification methods are difficult to implement in virtual environments, and users often value anonymity in the metaverse. Drawing on the Self-Concept Maintenance Theory and the Activation-Decision-Construction-Action Theory, we propose a method for the unobtrusive evaluation of movement data, which can be used for the early detection of fraudulent behavior due to altered cognitive dynamics. Our findings indicate that individuals moved significantly slower and over greater distances during the action of fraud but not during the fraud's activation, decision, or construction stages. In addition, our findings indicate that participants move more and slower in the stages before fraud than during fraud.