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

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