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1تقرير
المؤلفون: Sánchez, Pedro Miguel Sánchez, Celdrán, Alberto Huertas, Schenk, Timo, Iten, Adrian Lars Benjamin, Bovet, Gérôme, Pérez, Gregorio Martínez, Stiller, Burkhard
مصطلحات موضوعية: Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
الوصول الحر: http://arxiv.org/abs/2202.00137Test
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2دورية أكاديمية
المؤلفون: Sánchez Sánchez, Pedro Miguel, Huertas Celdran, Alberto, Schenk, Timo, Iten, Adrian Lars Benjamin, Bovet, Gérôme, Pérez, Gregorio Martínez, Stiller, Burkhard
المصدر: Sánchez Sánchez, Pedro Miguel; Huertas Celdran, Alberto; Schenk, Timo; Iten, Adrian Lars Benjamin; Bovet, Gérôme; Pérez, Gregorio Martínez; Stiller, Burkhard (2024). Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors. IEEE Transactions on Dependable and Secure Computing, 21(2):573-584.
مصطلحات موضوعية: Department of Informatics, 000 Computer science, knowledge & systems, Sensors, Fingerprint recognition, Data models, Behavioral science, Sensor phenomena and characterization, Robustness, Crowdsensing
وصف الملف: application/pdf
العلاقة: https://www.zora.uzh.ch/id/eprint/229220/1/2202.00137.pdfTest; urn:issn:1545-5971
الإتاحة: https://doi.org/10.5167/uzh-22922010.1109/TDSC.2022.3204535Test
https://www.zora.uzh.ch/id/eprint/229220Test/
https://www.zora.uzh.ch/id/eprint/229220/1/2202.00137.pdfTest -
3دورية أكاديمية
المؤلفون: Sánchez, Pedro Miguel Sánchez, Celdrán, Alberto Huertas, Schenk, Timo, Iten, Adrian Lars Benjamin, Bovet, Gérôme, Pérez, Gregorio Martínez, Stiller, Burkhard
المساهمون: Swiss Federal Office for Defense Procurement, University of Zürich UZH
المصدر: IEEE Transactions on Dependable and Secure Computing ; volume 21, issue 2, page 573-584 ; ISSN 1545-5971 1941-0018 2160-9209
مصطلحات موضوعية: Electrical and Electronic Engineering
الإتاحة: https://doi.org/10.1109/tdsc.2022.3204535Test
http://xplorestaging.ieee.org/ielx7/8858/10472294/09878222.pdf?arnumber=9878222Test
