IoT Trust Establishment Through System Level Interactions and Communication Attributes
Bibliographic record
Abstract
The integration of Internet of Things (loT)-based solutions in various applications introduced several challenges in security and privacy. Due to the nature of loT systems, traditional security solutions are not suitable for solving these challenges. Researchers introduced trust management as a viable solution due to its ability to track the dynamic behavior of loT devices. Compared to traditional security solutions, trust does not require an extensive amount of resources. Several loT trust solutions rely on distributed models that increase network overhead and consume additional energy. To this end, this work proposes a trust management scheme for loT systems that can be implemented at the access layer of loT systems. The proposed scheme establishes trust for loT devices through device-system interaction and communication attributes without requiring any additional information or modifications to the device. The trust value is computed using the trust attributes over a specific window size of interactions and using a forget factor. Simulation results show the ability of the proposed scheme to track the behavior of loT devices. Results also show that the proposed scheme maintains high performance in detecting persistent attacks compared to existing schemes from the literature, while improving the detection rate of ON-OFF attacks by 15%.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".