Abstract
This paper presents an architectural proposal for enhancing anomaly detection in the CyberSec4Europe project use case Open Banking. It proposes a trusted privacy-preserving ecosystem of threat intelligence platforms, based on MISP, to automatically exchange and process cyber threat information in an auditable and privacy-preserving manner. Additionally, a Federated Learning scheme is deployed to share machine learning models trained on a synthetic fraud transactions dataset, and the impact of data anonymization on model accuracy is measured and analyzed. This proposal provides a valuable contribution to the development of robust and efficient threat detection systems to enhance the resilience of organizations in the financial sector.
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Acknowledgments
This work has received funding from the Grant PID 2020-112675RB-C44 funded by MCIN/AEI/10.13039/501100011033. It has been also partially funded by the European Commission through the H2020 project CyberSec4Europe (g.a. 830929).
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Saura, P.F., Gil, J.F.M., Bernabé, J.B., Skarmeta, A. (2023). Privacy-Preserving Cyber Threat Information Sharing Leveraging FL-Based Intrusion Detection in the Financial Sector. In: Skarmeta, A., Canavese, D., Lioy, A., Matheu, S. (eds) Digital Sovereignty in Cyber Security: New Challenges in Future Vision. CyberSec4Europe 2022. Communications in Computer and Information Science, vol 1807. Springer, Cham. https://doi.org/10.1007/978-3-031-36096-1_4
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