Weighted-NEAT: An efficient weighted node evaluation scheme with assistant trust mechanisms to secure wireless ad hoc networks
Bibliographic record
Abstract
The pervasive nature of wireless devices and the arbitrary organization of mobile networks attract growing interest in the design of wireless and mobile ad hoc networks. However, malicious nodes always exist due to the vulnerabilities of wireless and mobile nodes and thereby their misbehavior can weaken the utilization of ad hoc networks. Therefore, addressing security issues becomes extremely important in such networks. In this article, we first describe the problem of misbehavior in wireless networks and state its challenges. Moreover, we propose a novel node evaluation solution in which a node can detect nodes' misbehavior through evaluating their trustworthiness and respond to the detected misbehavior accordingly. Thus, our scheme allows a mobile node to more effectively evaluate its neighbors based on the additional trust information from selected neighboring nodes. Finally, by means of the proof of system correctness and discussion, our node evaluation strategy is analyzed to show how it offers provable security properties, so it can thus enhance the security of a network and improve the effectiveness of node evaluation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".