# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 4 Authentication Methods in XR
### Inference-Based (Biometric) Authentication
#### 4.2.2 Behavioral Biometrics
The power control module communicates between the aggregator and the event monitor, and in the event of a negative classification decision by the aggregator, it pauses feature extraction and classification until the security risk is low. Data collection during the evaluation involved 32 college students, and their mechanism accuracy reached 99% using SVM classifiers. The authors also compared their accuracy results with the work in [141] and concluded that their mechanism achieved better performance. They mentioned their future plan to deploy GlassGuard on GG and evaluate long-term performance using other daily tasks. [PERSON] et al. [145] developed Nod to Auth, a nodding-based authentication technique, to address the issue of current cumbersome AR and VR input modalities. In a study with 10 participants using GC, the authors extracted bio-features using the embedded IMU device in a Huawei smartphone. Using an RF classifier, their authentication accuracy indicated an average accuracy of 97.01%, supporting the robustness of the model. They showed their interest in (1) deploying Nod to Auth for long-term evaluations, (2) considering the neck's elasticity features, and (3) using cameras for multi-model sensing in their future work. [PERSON] et al. [146,147] focused on user identifiability through motion data in VR. In [146], they specifically investigated privacy risks in social VR and the effect of duration and delay on identifiability by using a social interactions dataset of 232 subjects, ML methods, and a comparative study with different methods that resulted in high accuracy rates. They concluded that both the number and durability of recorded interaction sessions increase identifiability. However, lengthy delays between training and testing sessions decrease identifiability. In their subsequent work [147], they aimed at testing users' identifiability under typical VR conditions, unlike previous work where body motion was tested under custom-designed circumstances. Their system reached a 95% accuracy rate when trained and tested with a pool of 511 participants. They concluded that the limitation of their work is the loss of features due to dimensionality reduction and their non-generality to more complicated tasks. Thus, they recommended including other previously successfully used features like velocity, rotation, and acceleration to improve their work. [PERSON] et al. [148] proposed, investigated, and evaluated the potential of head movement-based continuous authentication in AR. Data collection involved only 5 participants to collect holographic head gaze data of AR users interacting with their environment, and ML algorithms were used to extract unique signatures from these AR users. They used ML models that are time series-based or feature-based and experimented with many feature extraction techniques. They evaluated their prototype, and the best accuracy results were achieved with the RF classifiers, with 92.675% accuracy and an 11% EER. They highlighted their interest in deploying their prototype and considering other holographic spatial interaction tasks. Based on work conducted by [PERSON] [147], [PERSON] et al. [149] demonstrated the extent to which head and hand motion data can uniquely identify a large number of users in VR. First, they used telemetry data from BeatLeader, an open-source dataset of over 50,000 users, for both motion and context feature extraction. Using a hybrid featurization technique that combines both motion and context features, several ML and DL classifiers were tested for pre-identification, and the best classifier was selected. The Light Gradient Boosting Machine (LightGBM) classifier outperformed other classifiers and reached 100% accuracy with a full hybrid featurization approach on 500 users. Due to the infeasibility of training LightGBM for very large datasets such as BeatLeader, the authors constructed a multi-layer hierarchal classifier for the purposes of training facilitation, scalability, and practicality.