A Real-Time Trust Management Model Using Digital Twin in IoT Networks
Bibliographic record
Abstract
The Internet of Things (IoT) is becoming more and more entwined with both our private lives and business environments. The IoT’s expanding relevance motivates researchers to develop models that examine IoT device activity as a means of determining trustworthiness and detecting unusual behavior. This paper aims to develop a new trust model based on digital twins to detect and foretell anomalies in real IoT setups. To build trust, twins communicate constantly and warn one another when their physical counterparts communicate. The notified twins then examine specific factors of the communicating nodes, such as traceability, residual energy, resource usage, etc., to detect anomalies and take appropriate actions. We evaluate the performance and applicability of our model using the iFogSim simulator, mainly considering the probability of detecting anomalies. The simulation demonstrates improvement in trust management, scalability, and resource efficiency achieving optimizing performance in terms of energy consumption, execution time, and network usage. We demonstrate around 95% accuracy rate in identifying compromised nodes, including during DoS attacks.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".