Performance evaluation of machine learning models for intrusion detection system

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dc.contributor.authorMohammed Badr Eddine, Laroussi Graine
dc.contributor.authorSupervisor: Lamri, Sayad
dc.date.accessioned2025-07-08T09:58:28Z
dc.date.available2025-07-08T09:58:28Z
dc.date.issued2025-06-15
dc.description.abstractIn order to improve cybersecurity, this final year project investigates the use of machine learning (ML) in intrusion detection systems (IDS). Its main goal is to create an intelligent intrusion detection system (IDS) that can accurately identify and categorize network intrusions. The study demonstrates the efficacy of these methods in enhancing threat detection and security resilience through the application and assessment of multiple machine learning models.
dc.identifier.urihttps://repository.univ-msila.dz/handle/123456789/46783
dc.language.isoen
dc.publisherMohamed Boudiaf University of M'sila
dc.titlePerformance evaluation of machine learning models for intrusion detection system
dc.typeThesis

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