DSpace Repository
Enhancing Cyber Attack Detection through Federated Machine Learning: A Privacy-Preserving Framework
JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Azer, Nabila | |
| dc.contributor.author | Benmerzoug, Djamel | |
| dc.date.accessioned | 2025-03-18T11:03:51Z | |
| dc.date.available | 2025-03-18T11:03:51Z | |
| dc.date.issued | 25/10/2024 | |
| 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.publisher | Université Frères Mentouri - Constantine 1 | |
| dc.title | Enhancing Cyber Attack Detection through Federated Machine Learning: A Privacy-Preserving Framework | fr_FR |
| dc.type | Article | fr_FR |
