Fuzzy trust recommendation based on collaborative filtering for mobile ad-hoc networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mobile ad-hoc networks (MANETs) are based on cooperative and trust characteristic of mobile nodes. Typically, nodes are both autonomous and self-organized without requiring a central administration or a fixed network infrastructure. Due to their distributed nature, MANETs are very vulnerable to various attacks. To enhance the security of MANETs, it is important to rate the trustworthiness of other nodes without central authorities to build up a trust environment. In this paper, we propose a fuzzy trust recommendation based on collaborative filtering, which stimulates collaboration among distributed computing and communicating nodes, facilitates the detection of untrustworthy nodes, and assists decision-making in various protocols for MANETs. Due to the uncertain interaction outcomes, we use fuzzy logic to model trust recommendation in a MANET environment. Our trust model combines direct trust and trust recommendation information based on collaborative filtering to allow nodes to represent and reason with uncertainty and imprecise information regarding other nodespsila trustworthiness. Simulation results show that the proposed model is flexible and valid.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it