D2D-MAP: A Drone to Drone Authentication Protocol Using Physical Unclonable Functions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it