Security, AI, IoT, machine learning
In recent years, the IoT device revolution has transformed our world into one where everything is connected, everything is smart, and everything is (or should be) secure. Connected devices do deliver clear benefits, but they also increase the risk of data manipulation, data theft and cyberattack. The lack of trust by businesses and consumers in smart, connected devices is a barrier. At this point, it is no longer sufficient to provide semiconductor components addressing these issues, there is an essential need to provide interoperable and resilient secure solutions and systems.
In this context, the SUNRISE project intends to develop a shared security solution to tackle these aspects via:
- Enabling the implementation of machine learning (ML) on the edge nodes facilitating IoT security analytics to defend against intrusion attacks and detect anomalies and misconfigurations.
- Enabling the sharing of relevant security data across different stakeholders via a cloud and/or Blockchain platform and by applying ML on the combined data and models.
- Evaluating homomorphic encryption as a privacy enhancing technology (PET) and applying ML on the combined encrypted datasets.
The BE partners (Engie Laborelec, NXP and Sirris) will demonstrate the SunRISE results in the context of an energy communities (smart grid) use case.
Project partners (Belgium)
September 2019 - August 2022
With the support of
Met de steun van
This is a cross-domain project in collaboration with the software engineering programme of Sirris.