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 |