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http://dx.doi.org/10.25673/121713| Titel: | Bio-Authenticated ECC Framework for Secure Drone-to-Drone Communication in IoD Environments |
| Autor(en): | Saare, Murtaja Ali |
| Körperschaft: | Hochschule Anhalt |
| Erscheinungsdatum: | 2025 |
| Umfang: | 1 Online-Ressource (7 Seiten) |
| Sprache: | Englisch |
| Zusammenfassung: | The Internet of Drones (IoD) has become a vital infrastructure to provide surveillance, transportation, and combat support, and therefore a compact, secure solution. This article introduces a new Bio-authenticated Elliptic Curve Cryptography Framework (ECC) that integrates biometric fuzzy extractors with ECC. The protocol removes the dependence on fixed credentials by binding authentication to unique biological features, thereby strengthening identity assurance and resistance to replay, impersonation, and stolen-verifier attacks. The process supports both sides achieving mutual authentication. Ephemeral session keys are generated without any Ground Control Station (GCS), thus providing some relief, although not enough as yet. Informal security analysis indicates that it can withstand commonly known threat vectors. Performance assessment reveals that the cost of communication and computation has been reduced by more than 28%, compared with existing ECC-based schemes. Cryptographic-biometric fusion substantially reduces storage overhead and raises resilience against insider threats as well. This privacy-preserving, lightweight protocol provides a secure solution scalable for real-world operations environments where computing resources are still very much lacking. |
| URI: | https://opendata.uni-halle.de//handle/1981185920/123665 http://dx.doi.org/10.25673/121713 |
| Open-Access: | Open-Access-Publikation |
| Nutzungslizenz: | (CC BY-SA 4.0) Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International |
| Enthalten in den Sammlungen: | International Conference on Applied Innovations in IT (ICAIIT) |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 1-1-ICAIIT_2025_13(4).pdf | 894.11 kB | Adobe PDF | ![]() Öffnen/Anzeigen |
Open-Access-Publikation
