Please use this identifier to cite or link to this item: http://dspace.univ-guelma.dz/jspui/handle/123456789/13426
Title: Detection des intrusions basée sur l’apprentissage automatique dans les systèmes IdO (Internet des Objets)
Authors: BOUKERTOUTA, MOHAMMED AMIN
Keywords: système de détection d’intrusion (IDS), L’apprentissage profond (DL), L’apprentissage automatique (ML), Classification, Régression.
Issue Date: 2022
Publisher: université de guelma
Abstract: Intrusion Detection System (IDS) is defined as a tool or software application which monitors the network or system activities and finds if there is any malicious activity. Outstanding growth and usage of internet raises concerns about how to communicate and protect the digital information safely. In today’s world hackers use different types of attacks for getting the valuable information. Many of the intrusion detection techniques, methods and algorithms help to detect those several attacks. The aim of this paper is to provide a complete study about the intrusion detection, types of intrusion detection methods, types of attacks, different tools and techniques to protect an IoT (Internet of Things) system from intrusion. We focus on using machine learning algorithms to detect intrusion, several algorithms have been studied to reach our conclusion.
URI: http://dspace.univ-guelma.dz/jspui/handle/123456789/13426
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