Design of Protection Mechanisms for the Internet of Drones
Bibliographic record
Abstract
The Internet of Drones (IoD) emerged as a novel mobile network paradigm. IoD is a unique environment with particular characteristics that differ from traditional ones (e.g., drones’ mobility and the fast network topology change), demanding compliance with security and privacy requirements. Likewise, IoD can suffer from novel drone-centered threats. The existent protection mechanisms (PMs) may not be adequate for the IoD environment since they may not embrace the IoD characteristics, also facing new threats. Therefore, the main goal of this dissertation is to study the design of PMs for the IoD, considering its particular characteristics. This study reveals a need to enhance current PMs to meet the IoD characteristics since they can not offer the same protection level. Our contributions advance the state-of-the-art on four fronts: new guidelines for IoD security and privacy field; novel location privacy PMs; novel anti-jamming PMs; and new strategies for automatic drone detection.
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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 | 0.000 | 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".