Metaverse Hierarchical T/Key-Based Lightweight Authentication Protocol
Bibliographic record
Abstract
Tremendous attention has been paid by researchers and developers in both industry and academia to the newly emerging network paradigm, the Metaverse. It is regarded as the next generation of fully immersive hyper-spatiotemporal virtual reality Internet. Users traverse the virtual world through head-mounted devices and are represented by avatars. However, with the rapid adoption of the metaverse, many security concerns have emerged, particularly after reports of misbehaving avatars. In this paper, we propose a secure mutual authentication protocol that allows avatars to securely authenticate and communicate with each other. To mitigate misbehaving incidents, the protocol allows traceability; hence, users of misbehaving avatars are recognized, reported, and held accountable for their actions. The protocol also provides unlinkability and anonymity for the avatars. The protocol is based on a hierarchical T/key, which is a time-based one-time password system. We formally prove the security properties of our protocol through theoretical analysis, BAN logic, and the AVISPA tool. On the other hand, the computational complexity of the verifier is one hash operation. Using a Raspberry Pi 4 Model B/8 G/Broadcom-BCM2711, Quad-core, Cortex-A72-1.5 GHz (ARM v8) 64-bit SoC processor, we computed the runtime for the protocol and compared it with other recently proposed protocols. Our comparative evaluation shows that our protocol outperforms other recently proposed schemes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".