Blockchain-Based Trust and Authentication Model for Detecting and Isolating Malicious Nodes in Flying Ad Hoc Networks
Bibliographic record
Abstract
Flying Ad Hoc Networks (FANET) is an emerging area of research due to its low cost, high coverage and fast transmission features. In these networks, the flying nodes are connected with ground stations and communicate wirelessly, especially when the networks are congested and complex. Due to mobility, and lack of predefined infrastructure, these networks have suffered from various security and trust issues. The traditional trust and security solutions are designed for ground networks and are not feasible for these networks. This paper proposes a trust and authentication model including Trust Establishment Mechanism for FANET (TEM-FANET) and authentication system by using Block-chain method. The trust is calculated to evaluate the node’s trust status and ensure the existence of the trustworthy nodes by using direct, indirect, and cumulative trust values. Whereas the authentication system is utilizing blockchain technology for nodes authentication and evaluate its feasibility. The proposed model is lightweight and able to monitor the node’s behavior and compute the trusted quality and broadcast the node status with neighbor nodes. The proposed model is also integrated with ground stations for record keeping and decision-making processes. The proposed model is evaluated in simulation with state-of-the-art trust solutions where the results show the better performance in terms of overhead, data delivery, node detection rate, and computational time.
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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 | 0.001 | 0.003 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".