Federated cyberattack detection for IoT-enabled Smart Cities
While attack detection is key to realize trustworthy smart cities, the use of large amounts of network traffic data by machine learning techniques can lead to privacy issues for citizens. To face this issue, we propose a federated learning approach in the context of Internet of Things-enabled smart cities integrating the Threat and Manufacturer Usage Description files as a prevention/mitigation approach.
MATHEU GARCIA Sara Nieves;
MÁRMOL CAMPOS Enrique;
HERNANDEZ RAMOS Jose Luis;
SKARMETA Antonio;
BALDINI Gianmarco;
2022-12-01
IEEE COMPUTER SOC
JRC129318
0018-9162 (online),
https://ieeexplore.ieee.org/document/9963740,
https://publications.jrc.ec.europa.eu/repository/handle/JRC129318,
10.1109/MC.2022.3195054 (online),
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