Modern vehicles are becoming increasingly capable of establishing connectivity that allows the rich-information exchange between them and road infrastructure units. The so-called ‘connected-vehicles’ paradigm can bring-about an enormous potential towards safer transportation only if the involved security needs against a variety of ‘new’ threats are confidently satisfied.
- Modern connected vehicles integrate 3rd party components and applications
- Numerous interfaces and an increased attack surface is exposed
- Well-known approaches to security assurance are not tailored for connected vehicles and come with high cost
What SAFERtec achieved
- FOCUSED on vehicles and associated connectivity
- EXPLORED the involved vulnerabilities of connected vehicles
- APPLIED innovative techniques for attack modelling
- VALIDATED a framework for the quantification of security assurance levels
- CONTRIBUTED to relevant standards
Our role in the project
We were involved in the development of a new methodology of risk analysis adapted to the threats targeting connected vehicles. This methodology allowed us to deduce security and privacy requirements, to provide guidelines to implement them and so to obtain a secure and privacy centric ITS station.
We also performed penetration tests and vulnerability research on V2X (Vehicle to Everything) solutions to validate the level of security assurance provided by the assurance framework developed in the project. In addition, we enabled competence development on the embedded systems for connected automobile safety.
Use case: optimal driving speed advice
|In this use case, the vehicle relies on the received data of the traffic light or of the cloud to calculate the appropriate speed for reaching the intersection at the beginning of the next green phase. The challenge here is among others to ensure that an attacker cannot send fake data to the vehicle and so that he can not create a collision at the intersection.|
|HORIZON 2020: ec.europa.eu/programmes/horizon2020
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732319.