1. This article proposes RubikBiom, a knowledge-driven behavioural biometric authentication scheme for authentication in VR.
2. It leverages hand movement patterns performed during interactions with a knowledge-based authentication scheme (e.g., when entering a PIN) to establish an additional security layer.
3. The study achieved an accuracy of up to 98.91% by applying a Fully Convolutional Network (FCN) on 32 authentications per subject.
The article is generally trustworthy and reliable, as it provides evidence for its claims and presents both sides of the argument equally. The authors provide detailed information about their research methodology, including the demographics of the participants, data collection procedure, and results from their study. Furthermore, they cite relevant literature to support their claims and provide references for further reading.
However, there are some potential biases that should be noted in the article. For example, the authors do not mention any potential risks associated with using RubikBiom or other knowledge-driven behavioural biometric authentication schemes in VR environments. Additionally, they do not explore any counterarguments or alternative solutions to the problem they are addressing in this article. Finally, there is no discussion of how RubikBiom could be improved or adapted for different contexts or applications in VR environments.