Complete subject available here: https://sdrive.cnrs.fr/apps/files/?dir=/&openfile=161687433
Scientific context
This PhD is in the frame of the ANR Junior Professor Chair on Autonomous and Secure Swarm of Maritime Drones. The Chair aims at providing trust and security on maritime swarm of drones, a strategic and major issue in the current socio-political context. In this context, this PhD will investigate drones vulnerability to external interference and will contribute to their resilience including both, reliability and security. In particular, one major objective is to develop and implement efficient embedded detection systems able to distinguish drone deviant behavior caused by errors or attacks. The usage of drones, aerial or maritime (surface or underwater) is becoming more and more widespread in various domains, recreational and critical. A deviant behavior can compromise the drone (critical) mission, cause collateral damage and/or compromise intellectual property privacy. It is therefore crucial to being ableto detect possibly deviant behavior and to determine if a drone is under an attack.
A wide range of intrusion detection systems exist, in particular to detect network-based attacks in the context of the internet of things. However, these are not specifically designed for drones and therefore cannot simply be applied and be efficient to their specific threats and behavior. Indeed, the threat surface of drones is rapidly evolving and requires to take into account threats at the system level (for a drone as a connected, intelligent, autonomous system), rather than solely at the network level. In this sense recent work [Est19] has shown that aerial drones can be attacked even during the flight with a sort of directed weapon, therefore silently modifying some parameters as their battery temperature and remanent information in their flight logs. The recent work considering drones focuses on a particular compromised sensor or on a particular attack, and still requires to consider the drone in its whole [HGM23].
Objectives and Work Program
The main objective of this PhD work is contributing to secure drones to make them more reliable and trustable. In particular, this work will be focused on increasing drones resilience against external disturbances (malicious or not). The main tasks are as follow:
the study of the panorama of threats induced by external disturbances and the analysis of their effect from a system point of view (malfunction of a sensor, or error in making a decision, etc.). We will focus our interest on maritime drones, particularly exposed to physical proximity to a malicious entity (person or drone), as well as to specific operational conditions (water projection, reflection, etc) that can have an impact on the reliability of the drone’s mission.
defining and integrating metrics for detecting and analysing deviant behaviour in a reliability and cybersecurity context. Metrics can be software of hardware and should be able to capture the possible disturbances effect, such as on the drone’s autonomous decision making, widely based on the environment caption, remanent logs specific to the drone mission and flight tracking system, etc.
the design and implementation of effective and efficient systems for detecting deviant behaviour embedded on the drone. Approaches based on multi sensors, as well on the prediction of sensor data readings and drone’s decision making will be considered.
evaluating the feasibility and efficiency of the proposed solutions though their implementation on real drone systems. This task will benefit of the drones platforms and expertise of Lab-STICC laboratory, Lorient.
Candidate Profile
- Graduate: candidate must hold a Master’s degree or an Engineer degree in Computer/Electrical Engineering, Computer Science, Embedded Systems, or similar. The required skills are:
- Good knowledge in C/C++/Python
- Good knowledge in Embedded Systems and Operating System Development
- Good knowledge in Computer Architecture
- Cybersecurity knowledge, skills are not mandatory, but interest in this topic is required
- Date and place
- Applications are open until April.
- Although flexible, this PhD is expected to start from October 2025 for a 3-year duration.
- The PhD student will work within SHAKER and ARCAD teams of the Lab-STICC laboratory in Lorient, France and will benefit of SHAKER team drone expertise and platforms, as well as of the ARCAD team expertise in cybersecurity
How to apply
Please send your CV and available Master/Engineering transcripts to:
- Maria Méndez Real - maria.mendez-real@univ-ubs.fr
- Vianney Lapotre - vianney.lapotre@univ-ubs.fr
- Dominique Heller - dominique.heller@univ-ubs.fr
References
[Est19] José Lopes Esteves. Electromagnetic watermarking: Exploiting iemi effects for forensic tracking of uavs. In 2019 International Symposium on Electromagnetic Compatibility-EMC EUROPE, pages 1144–1149. IEEE, 2019.
[HGM23] Raby Hamadi, Hakim Ghazzai, and Yehia Massoud. Reinforcement learning based intrusion detection systems for drones: A brief survey. In 2023 IEEE International Conference on Smart Mobility (SM), pages 104–109. IEEE, 2023.