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Les systèmes de détections d’intrusion basés sur L’ensemble machine learning

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dc.contributor.author BAYMOUT, Mohamed Tayeb
dc.contributor.author BENMERIEM Abdelouahab, Abdelouahab
dc.date.accessioned 2024-09-25T09:09:59Z
dc.date.available 2024-09-25T09:09:59Z
dc.date.issued 2024
dc.identifier.issn MM/832
dc.identifier.uri https://dspace.univ-bba.dz:443/xmlui/handle/123456789/5484
dc.description.abstract This paper examines the different techniques employed in intrusion detection systems, with a focus on Machine Learning. Following this, three ensemble methods in machine learning: Bagging, Boosting, and Voting are introduced. These methods aim to enhance model efficiency by merging several individual models. Finally, a comparison based on accuracy rate is established among these three methods en_US
dc.language.iso fr en_US
dc.publisher UNIVERSITY BBA en_US
dc.subject systèmes de détection d’intrusion, Machine Learning, Bagging, Boosting, Voting. en_US
dc.title Les systèmes de détections d’intrusion basés sur L’ensemble machine learning en_US
dc.type Thesis en_US


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