Trust Management for Security Enhancements in Ad hoc Networking Paradigms with Uncertain Reasoning
Bibliographic record
Abstract
Trust-based schemes are promising techniques to tackle inside attacks in distributed self-organized networks, such as mobile ad hoc networks and vehicular ad hoc networks. For the outside attackers, access control, authorization and authentication by cryptography can effectively thwart most of them. For the inside attackers, prevention based schemes such as cryptographic techniques are usually powerless. In the trust management system, trust is defined as the degree of belief that an entity can behave correctly in an observer's perspective. Compared to prevention-based schemes, detection-based schemes, such as trust management, dynamically estimate the internal nodes behavior. Based on the results of the estimation, the detection system makes the decision whether the node is a malicious attacker. These detectionbased schemes introduce a large amount of uncertainties due to the unpredictable behavior of each node in the networks. Therefore, in the trust management system, accurate trust assessment is playing a key role in the trust management. It is significantly affected by uncertainty. In order to obtain accurate trust of each entity in the network, we apply uncertain reasoning, coming from the artificial intelligence field, to trust management in the emerging networking paradigms.
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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 | 0.001 | 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 | 0.001 | 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".