Behavioral authentication has become increasingly popular as a natural method for authentication in Virtual Reality (VR). However, existing studies often overlook the fact that users may perform behavioral authentication in different postures (i.e., sitting, standing, reclining) during VR use. Therefore, understanding how posture variations affect classification accuracy is crucial for designing posture-robust systems. In this study, we conducted a controlled experiment (N = 30) to investigate the impact of posture on classification accuracy during a target-selection task. We collected behavioral trajectory data and analyzed it using multivariate time series classification algorithms, addressing authentication performance under three different postures. In a within-posture authentication, reclining took longer but achieved the highest classification accuracy, with an interaction effect between posture and target vertical layout. In cross-posture authentication, transfers from sitting to standing/reclining were more effective than direct transfers between standing and reclining, with vertical layout crucial for classification accuracy. In mixed-posture training, the cross-posture classification accuracy increased, particularly when standing and reclining data were combined to help the model indirectly learn features of sitting posture. These findings provide valuable insights for designing tasks and data collection strategies that support the development of robust cross-posture authentication systems.

Current behavioral authentication systems in Virtual Reality (VR) require sustained focused interaction during task execution - an assumption frequently incompatible with real-world constraints across two factors: (1) physical limitations (e.g., restricted hand/eye mobility), and (2) psychological barriers (e.g., task-switching fatigue or break-in-presence). To address this attentional gap, we propose a design framework bridging focused and casual interactions in behavior-based VR authentication (Casual-VRAuth). Based on this framework, we designed an authentication prototype using a modified ball-and-tunnel task (propelling a ball along a circular path), supporting three interaction modes: baseline Touch, and two eyes-free options (Hover/Tapping). Experimental results demonstrate that our framework effectively guides the design of authentication systems with varying interaction engagement levels (Touch > Hover > Tapping) to accommodate scenarios requiring casual interaction (e.g., multitasking or eyes-free operation). Furthermore, we revealed that reducing interaction engagement enhances resistance to mimicry attacks while decreasing cognitive workload and error rates in multitasking or eyes-free environments. However, this approach compromises the average classification accuracy of Interaction behavior under different algorithms (including InceptionTime, FCN, ResNet, CNN, MLP, and MCDCNN). Notably, moderate reduction of interaction engagement enhances authentication speed, while excessive reduction may conversely slow it down. Overall, our work establishes a novel design paradigm for VR authentication that supports casual interactions and offers valuable insights into balancing usability and security.