An Efficient Authentication Scheme Using Blockchain as a Certificate Authority for the Internet of Drones
Bibliographic record
Abstract
The Internet of Drones (IoD) has recently gained popularity in several military, commercial, and civilian applications due to its unique characteristics, such as high mobility, three-dimensional (3D) movement, and ease of deployment. Drones, on the other hand, communicate over an unencrypted wireless link and have little computational capability in a typical IoD environment, making them exposed to a wide range of cyber-attacks. Security vulnerabilities in IoD systems include man-in-the-middle attacks, impersonation, credential leaking, GPS spoofing, and drone hijacking. To avoid the occurrence of such attacks in IoD networks, we need an extremely powerful security protocol. To address these concerns, we propose a blockchain-based authentication scheme employing Hyperelliptic Curve Cryptography (HECC). The concepts of a blockchain as a Certificate Authority (CA) and a transaction as a certificate discussed in this article are meant to facilitate the use of a blockchain without CAs or a Trusted Third Party (TTP). We offer a security analysis of the proposed scheme, which demonstrates its resistance to known and unknown attacks. The proposed scheme resists replay, man-in-the-middle, device impersonation, malicious device deployment, Denial-of-Service (DoS), and De-synchronization attacks, among others. The security and performance of the proposed scheme are compared to relevant existing schemes, and their performance is shown to be better in terms of security attributes as well as computation and communication costs than existing competitive schemes. The total computation cost of the proposed scheme is 40.479 ms, which is 37.49% and 49.79% of the two comparable schemes. This shows that the proposed scheme is better suited to the IoD environment than existing competitive schemes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".