Résumé
The use of drones has become widespread in many fields, including critical ones. It means that these devices need to be protected against cyber attacks, and more generally, against contingencies, with a view to infrastructure resilience.This study simulates the operations of a drone fleet and assesses the impact of potential attacks or threats. It leverages the STRIDE threat modeling framework, which categorizes six types of threats: impersonation, tampering, repudiation, information disclosure, denial of service, and privilege escalation. Autonomous underwater vehicles (AUVs) are available, affordable, and can play a critical role for many applications; therefore, the systems we chose as analysis targets integrate AUVs and USVs as physical components, as well as digital avatars. This work is based on a model-based systems engineering (MBSE) approach. The use of models facilitates interoperability with third-party tools, enabling hybrid simulations and flexible integration of new devices, as well as global state capture via evaluators. Moreover, the use of well-established software design patterns ensures the modularity, reusability, and maintainability of the environment.Currently, our case study is to map an area of interest using a fleet of virtual drones, prior to deploying a mixed infrastructure. The process starts with data acquisition, followed by the refinement of data into information, then into knowledge, assuming the zone has been previously covered by a hydrographic study, and finally provides mission feedback. The potential risk lies in the manipulation of data or disruption of the drones' functionalities (such as movement, communication, etc.), which could compromise the mission's success and the integrity of the infrastructure.Future work will extend this framework to advanced threat and risk assessments (TARA), taking partial failures into account, exploring complex scenarios and drawing on more comprehensive metrics to propose appropriate security enhancement measures.