A probabilistic-based approach towards trust evaluation using Poisson Hidden Markov Models and Bonus Malus Systems
Bibliographic record
Abstract
In the paper, the uncertainty of trust is transformed into a probability vector denoting the probability distribution over possible trust levels of an entity that is hidden from observation but determined by its expected performance. We propose the use of Poisson Hidden Markov Models (PHMMs) for estimating the trust for entities in wireless environments, in which the Poisson distribution is used to describe the occurrences of behavioral patterns in peer-to-peer interactions. PHMMs allow us to explicitly consider an entity's unobserved trustworthiness that influences it's observed behaviors. As well, the hidden Markov process is associated with a Bonus-Malus System that is used to reduce the computational complexity of parameter estimations involved. An application of the model in the scenario of detection of probabilistic packet dropping attack has been investigated. The simulations demonstrate that the approach is capable of accurately estimating the (hidden) trust states probability distribution as well as the expected performance for the entities in the networks through their observed behaviors.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 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 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".