Access Control Protocol for Battlefield Surveillance in Drone-Assisted IoT Environment
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
Surveillance drones, called as unmanned aerial vehicles (UAVs), are aircrafts that are utilized to collect video recordings, still images, or live video of the targets, such as vehicles, people or specific areas. Particularly in battlefield surveillance, there is high possibility of eavesdropping, inserting, modifying or deleting the messages during communications among the deployed drones and ground station server (GSS). This leads to launch several potential attacks by an adversary, such as main-in-middle, impersonation, drones hijacking, replay attacks, etc. Moreover, anonymity and untraceability are two crucial security properties that need to be maintained in battlefield surveillance communication environment. To deal with such a crucial security problem, we propose a new access control protocol for battlefield surveillance in drone-assisted Internet of Things (IoT) environment, called ACPBS-IoT. Through the detailed security analysis using formal and informal (nonmathematical), and also the formal security verification under automated software simulation tool, we show that the proposed ACPBS-IoT can resist several potential attacks needed in a battlefield surveillance scenario. Furthermore, the testbed experiments for various cryptographic primitives have been performed for measuring the execution time. Finally, a detailed comparative study on communication and computational overheads, and security, as well as functionality features, reveals that the proposed ACPBS-IoT provides superior security and more functionality features, and better or comparable overheads than other existing competing access control schemes.
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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 it