DATE Save the Date 17 to 19 April 2023

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DATE Sponsors and Executive Committees.

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Artificial Intelligence based cybersecurity for connected and automated vehicles

Project Acronym
CARAMEL
Project Type
EU-funded projects (H2020/Horizon Europe)
Project Dates
-
Project Website
https://www.h2020caramel.eu
Contact Person
Jordi Guijarro, i2cat, jordi.guijarro@i2cat.net
  1. What makes the project concept unique?
    In the CARAMEL project, we strive to implement machine-learning based detection of attacks against the connected and/or autonomous vehicle by analysing sensor data, V2X data, and the status of embedded controllers like the OBU (on-board unit) in real time using a tamper-proof device that directly integrated into the car - the anti-hacking device (AHD). This architecture and concept is novel and innovative, and CARAMEL is the first project to implement and demonstrate this.
  2. What project outcomes can be of use to the DATE 2022 community?
    Even as CARAMEL implements its concepts and architectures in the automotive space, the key CARAMEL innovations are transferable to other IoT and Embedded applications domains as well, such as factory floors, building automation systems or others. Therefore, the CARAMEL presentation and presence of CARAMEL representatives during the conference would be of value to the DATE 2022 community.
  3. What inputs (solutions) are expected from the DATE 2022 community?
    In the CARAMEL project, several integration options for the anti-hacking device based on different IoT devices have been pursued already. However, in order for the concept to be commercially viable and cost-effective, the concept of a machine-learning-based intrusion device must be even better integrated into commercial offerings for the Automotive and IoT market. The DATE 2022 community could provide valuable input for that endeavour.
  4. What new research topics and trends the project introduces?
    CCAM and IoT both will face important security challenges in the future as bad actors discover these new areas for their activities. Since bad actors will use machine learning to subvert machine-learning based processes and algorithms in the CCAM and IoT world (eg. using Generative Adversarial Networks (GAN)), a trend in the security industry is also to use machine learning to detect and counter these attacks. The CARAMEL project showcases this approach in the Automotive context.

Project partners:

  • Fundació Privada i2CAT, Internet i Innovació digital a Catalunya, Spain
  • DEUTSCHE TELEKOM SECURITY GMBH, Germany
  • ALTRAN DEUTSCHLAND SAS & CO KG, Germany
  • EIGHT BELLS LTD, Cyprus
  • UBIWHERE LDA, Portugal
  • CYBERLENS BV, Netherlands
  • GREENFLUX ASSETS BV, Netherlands
  • SIDROCO HOLDINGS LIMITED, Cyprus
  • 0 INFINITY LIMITED, UK
  • UNIVERSITY OF CYPRUS, Cyprus
  • University of Patras, Greece
  • Idneo Technologies SAU, Spain
  • AVL LIST GmbH, Austria
  • PANASONIC AUTOMOTIVE SYSTEMS EUROPE GMBH, Germany
  • ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE, South Korea
  • KOREA AUTOMOTIVE TECHNOLOGY INSTITUTE, South Korea
  • MOBIGEN CO LTD, Republic of Korea
  • ATOS IT SOLUTIONS AND SERVICES IBERIA SL, Spain