A Secure and Trusted Channel Protocol for UAVs Fleets

  • Raja Naeem Akram
  • Konstantinos Markantonakis
  • Keith Mayes
  • Pierre-François Bonnefoi
  • Amina Cherif
  • Damien SauveronEmail author
  • Serge Chaumette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10741)


Fleets of UAVs will be deployed in near future in reliability and safety critical applications (e.g. for smart cities). To satisfy the stringent level of criticality, each UAV in the fleet must trust the other UAVs with which it communicates to get assurance of the trustworthiness in information received and to be sure not to disclose information to an unauthorized party. In addition, to be protected against an attacker willing to eavesdrop and/or modify the exchanged data, the communication channel needs to be secured, i.e. it has to provide confidentiality and integrity of exchanges. The work presented here is based on our previous research which concluded that it is required that each UAV includes a Secure Element (which we called ARFSSD standing for Active Radio Frequency Smart Secure Device) to withstand an adversary with a high attack potential. In this paper, we propose a secure and trusted channel protocol that satisfies the stated security and operational requirements for a UAV-to-UAV communication protocol. This protocol supports three main objectives: (1) it provides the assurance that all communicating entities can trust each other and can trust their internal (secure) software and hardware states; (2) it establishes a fair key exchange process between all communicating entities so as to provide a secure channel; (3) it is efficient for both the initial start-up of the network and when resuming a session after a cold and/or warm restart of a UAV. The proposed protocol is formally verified using CasperFDR and AVISPA.


Unmanned Aerial Vehicles (UAVs) UAVs Fleet Automated Validation Of Internet Security Protocols And Applications (AVISPA) High Onset Potential Secure Channel Protocol (SCP) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors from XLIM acknowledge the support of:

– the SFD (Security of Fleets of Drones) project funded by Région Limousin;

– the TRUSTED (TRUSted TEstbed for Drones) project funded by the CNRS INS2I institute through the call 2016 PEPS (“Projet Exploratoire Premier Soutien”) SISC (“Sécurité Informatique et des Systèmes Cyberphysiques”);

– the SUITED (Suited secUrIty TEstbed for Drones), SUITED2 and UNITED (United NetworkIng TEstbed for Drones), UNITED2 projects funded by the MIRES (Mathématiques et leurs Interactions, Images et information numérique, Réseaux et Sécurité) CNRS research federation.

The authors from LaBRI acknowledge the support of:

– the TRUSTED (TRUSted TEstbed for Drones) project funded by the CNRS INS2I institute through the call 2016 PEPS (“Projet Exploratoire Premier Soutien”) SISC (“Sécurité Informatique et des Systèmes Cyberphysiques”);

– the SUITED-BX, SUITED2-BX and UNITED-BX, UNITED2-BX projects funded by LaBRI and its MUSe team.


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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Raja Naeem Akram
    • 1
  • Konstantinos Markantonakis
    • 1
  • Keith Mayes
    • 1
  • Pierre-François Bonnefoi
    • 2
  • Amina Cherif
    • 2
    • 4
  • Damien Sauveron
    • 2
    • 3
    Email author
  • Serge Chaumette
    • 3
  1. 1.Information Security Group Smart Card CentreRoyal Holloway, University of LondonEghamUK
  2. 2.XLIM (UMR CNRS 7252/Université de Limoges), MathISLimogesFrance
  3. 3.LaBRI (UMR CNRS 5800/Université de Bordeaux)TalenceFrance
  4. 4.LARI (Université Mouloud Mammeri de Tizi-Ouzou)Tizi-OuzouAlgeria

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