Next-Gen Metaverse Security Through Intrusion Detection Enhanced by Transformers and GANs
Bibliographic record
Abstract
As the metaverse grows in popularity and complexity, securing its virtual environment is critical. Metaverse intrusion detection involves identifying and preventing unauthorized access, malicious activities, and potential threats. To address these challenges, we propose a novel Metaverse intrusion detection system (MIDS) that combines generative adversarial networks (GAN) and Transformer-based classifiers. The system operates in three stages: 1) generating diverse and realistic network traffic using GAN; 2) detecting intrusions with a Transformer-based classifier; and 3) ensuring data privacy through federated learning and a trusted authority mechanism. Unlike traditional methods, our approach employs dual aggregation, generating both global and local models tailored to users’ needs. Tested on public datasets, the method achieves state-of-the-art performance with an F1-score of 0.9984, demonstrating its effectiveness in generating realistic training data and improving MIDS performance. This approach can extend to other security domains requiring diverse data for training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".