contINuous deCentralized lEarNing of ioT devIces' behaVioural profilEs

MSCA (Marie Skłodowska-Curie)HORIZON-TMA-MSCA-PF-EFID: 101065524
EC Contribution
€1,812
Consortium Size
3 orgs
Start Year
2023
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 candidates 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.

Consortium (3)

Project Results (7)

Source: CORDIS, the EU research results database.

Publications (4)
Federated learning for misbehaviour detection with variational autoencoders and Gaussian mixture models
International Journal of Information Security· 2025DOI
Enrique Mármol Campos, Aurora Gonzalez-Vidal, José L. Hernández-Ramos, Antonio Skarmeta
FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning
IEEE Transactions on Emerging Topics in Computing· 2025DOI
Enrique Mármol Campos, Aurora Gonzalez-Vidal, José L. Hernández-Ramos, Antonio Skarmeta
Intrusion Detection Based on Federated Learning: A Systematic Review
ACM Computing Surveys· 2025DOI
Jose Hernandez-Ramos, Georgios Karopoulos, Efstratios Chatzoglou, Vasileios Kouliaridis, Enrique Marmol, Aurora Gonzalez-Vidal, Georgios Kambourakis
Misbehavior detection in intelligent transportation systems based on federated learning
Internet of Things· 2024DOI
Enrique Mármol Campos, José L. Hernandez-Ramos, Aurora González Vidal, Gianmarco Baldini, Antonio Skarmeta
Deliverables (2)
Other Results (1)
Periodic Reporting for period 1 - INCENTIVE (contINuous deCentralized lEarNing of ioT devIces' behaVioural profilEs)