D2D-MAP: A Drone to Drone Authentication Protocol Using Physical Unclonable Functions
Bibliographic record
Abstract
With the continuous miniaturization of electronic devices and the recent advancements in wireless communication technologies, Unmanned Aerial Vehicles (UAVs), in general, and Small Unmanned Aerial Vehicles (SUAVs, a.k.a., drones), in particular, are becoming progressively used by the civilian sector within the context of a variety of applications, bringing great convenience to the public. However, due to their resource-constrained nature, risky environmental application, and wireless way of communication, drones are not immune from cyberthreats. As a consequence, the security of drones (SUAVs) has recently gained significant attention by the research community. In particular, when it comes to inter-drone communication. Although traditional cryptographic techniques may provide a certain level of security, they actually constitute a heavy burden on SUAVs due to their resource-constrained nature. In the light of enforcing the security of inter-drone communications, this paper proposes a lightweight drone-to-drone authentication protocol, called D2D-MAP, that uses PUF (Physical Unclonable Function) technology. We design the protocol and evaluate its resilience against various attacks. We use resource-constrained hardware to implement the authentication protocol and perform an evaluation of its performance. We show that the protocol's security and the obtained performance are prominent compared to state-of-the-art authentication protocols, and that they conform to SUAVs security and performance requirements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".