Key revocation based on Dirichlet multinomial model for mobile ad hoc networks
Bibliographic record
Abstract
The absence of an online trusted authority makes the issue of key revocation in mobile ad hoc networks (MANETs) particularly challenging. In this paper, we present a novel self-organized key revocation scheme based on the Dirichlet multinomial model and identity-based cryptography (IBC). Our key revocation scheme offers a theoretically sound basis for a node in MANETs to predict the behavior of other nodes based on its own observations and reports from peers. In our scheme, each node keeps track of three categories of behavior defined and classified by an external trusted authority, and updates its knowledge about other nodespsila behavior with 3-dimension Dirichlet distribution. Differentiating between suspicious behavior and malicious behavior enables nodes to make multilevel response by either revoking keys of malicious nodes or ceasing the communication with suspicious nodes for some time to gather more information for making further decision. Furthermore, we also analyze the attack-resistant properties of our key revocation scheme through extensive simulations in the presence of adversaries.
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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 | 0.000 |
| 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 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".