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Enhancing Cyber Attack Detection through Federated Machine Learning: A Privacy-Preserving Framework

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dc.contributor.author Azer, Nabila
dc.date.accessioned 2025-03-18T11:03:51Z
dc.date.available 2025-03-18T11:03:51Z
dc.date.issued 2024
dc.identifier.uri http://depot.umc.edu.dz/handle/123456789/14541
dc.description.abstract 23 Corresponding author email : azeri.nabila@gmail.com Enhancing Cyber Attack Detection through Federated Machine Learning: A Privacy-Preserving Framework Azeri, Nabila*1; Benmerzoug, Djamel2 1Abbes Laghrour University, Khenchela 2Université Abdelhamid Mehri - Constantine 2 Abstract In this paper, we propose a novel framework for enhancing cyber attack detection through the application of Federated Machine Learning (FML). As cyber threats become increasingly sophisticated and frequent, traditional machine learning approaches that centralize data collection present significant privacy and security challenges fr_FR
dc.title Enhancing Cyber Attack Detection through Federated Machine Learning: A Privacy-Preserving Framework fr_FR
dc.type Article fr_FR


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