Summary
In an increasingly hyperconnected society, cybersecurity concerns take on a broader dimension, which can impact citizens’ safety. This has been widely recognized in the EU through specific legal instruments, such as the Cybersecurity Act and the recent Cybersecurity Strategy for the Digital Decade. The development of the IoT technologies have fostered the deployment of data-driven services for everyday scenarios, such as transport, health and energy, but have also increased the probability and impact of new cybersecurity threats. Recent and well-known attacks have demonstrated the need to develop AI-based techniques to identify such attacks in IoT scenarios. In this context, the objective of this project is to build a decentralized framework to learn the IoT devices’ intended behavior throughout their lifecycle, in order to distinguish abnormal behavior that may reflect a security attack or threat. The proposal will build an edge-based and federated learning architecture to enable IoT devices to participate in the learning process by using lightweight security protocols. This architecture will include the use of blockchain through a novel approach in which different ledgers will be interconnected to improve the performance and practicability of existing approaches. Furthermore, the proposal will be the first effort to quantify the impact of human-machine interactions on the devices’ intended behavior. The project will be validated quantitatively and qualitatively to identify potential tradeoffs for its deployment in different IoT-enabled scenarios. It will leverage the candidate’s extensive knowledge and experience in IoT cybersecurity, which will be strengthened by the host institution to improve his technical and transferable skills in a multidisciplinary environment. The proposed project represents a unique contribution to reinforcing the culture of cybersecurity in the EU that will boost the candidate's professional development during and after the fellowship.
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| Web resources: | https://cordis.europa.eu/project/id/101065524 |
| Start date: | 01-04-2023 |
| End date: | 28-02-2025 |
| Total budget - Public funding: | - 181 152,00 Euro |
Cordis data
Original description
In an increasingly hyperconnected society, cybersecurity concerns take on a broader dimension, which can impact citizens’ safety. This has been widely recognized in the EU through specific legal instruments, such as the Cybersecurity Act and the recent Cybersecurity Strategy for the Digital Decade. The development of the IoT technologies have fostered the deployment of data-driven services for everyday scenarios, such as transport, health and energy, but have also increased the probability and impact of new cybersecurity threats. Recent and well-known attacks have demonstrated the need to develop AI-based techniques to identify such attacks in IoT scenarios. In this context, the objective of this project is to build a decentralized framework to learn the IoT devices’ intended behavior throughout their lifecycle, in order to distinguish abnormal behavior that may reflect a security attack or threat. The proposal will build an edge-based and federated learning architecture to enable IoT devices to participate in the learning process by using lightweight security protocols. This architecture will include the use of blockchain through a novel approach in which different ledgers will be interconnected to improve the performance and practicability of existing approaches. Furthermore, the proposal will be the first effort to quantify the impact of human-machine interactions on the devices’ intended behavior. The project will be validated quantitatively and qualitatively to identify potential tradeoffs for its deployment in different IoT-enabled scenarios. It will leverage the candidate’s extensive knowledge and experience in IoT cybersecurity, which will be strengthened by the host institution to improve his technical and transferable skills in a multidisciplinary environment. The proposed project represents a unique contribution to reinforcing the culture of cybersecurity in the EU that will boost the candidate's professional development during and after the fellowship.Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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