Intrusion detection for the Internet of Things using deep learning techniques / nour el imene Lakabi  

Public ISBD Titre : Intrusion detection for the Internet of Things using deep learning techniques Type de document : texte imprimé Auteurs : nour el imene Lakabi, Auteur ; Heithem Zeroual, Auteur ; Samir Fenanir, Directeur de thèse Année de publication : 2023 Importance : 1 vol (69 f .) Format : 29cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Internet of Things (IoT) intrusion detection Index. décimale : 004 Informatique Résumé : In response to the increasing concerns about security in the Internet of Things (IoT), this study focuses on addressing the crucial issue of intrusion detection using deep learning techniques. The primary goal is to develop effective and reliable intrusion detection systems capable of accurately identifying intrusions in IoT networks. The research begins by providing an in-depth introduction to the IoT, encompassing its architecture, data flow, components, and application areas. Key challenges faced by the IoT, including security, interoperability, scalability, and energy efficiency, are highlighted. Emphasis is placed on the importance of IoT security, vulnerabilities, and the various types of attacks that can occur. Intrusion Detection Systems (IDS), such as signature-based, anomaly-based, and specification-based IDS, are introduced as essential tools for detecting and mitigating intrusions. The thesis presents the implementation of three deep learning-based intrusion detection programs (DNN, CNN, and RNN) using the PyTorch library, with the effectiveness of these programs evaluated using the IoTID20 dataset. By harnessing deep learning techniques, this research contributes to enhancing the understanding and effectiveness of intrusion detection in IoT networks. Côte titre : MAI/0707 En ligne : https://drive.google.com/file/d/1m2FHecjMZFFPzXSDnF_pmOhCPeKjSQYh/view?usp=drive [...] Format de la ressource électronique : pdf Intrusion detection for the Internet of Things using deep learning techniques [texte imprimé] / nour el imene Lakabi, Auteur ; Heithem Zeroual, Auteur ; Samir Fenanir, Directeur de thèse . - 2023 . - 1 vol (69 f .) ; 29cm. Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Internet of Things (IoT) intrusion detection Index. décimale : 004 Informatique Résumé : In response to the increasing concerns about security in the Internet of Things (IoT), this study focuses on addressing the crucial issue of intrusion detection using deep learning techniques. The primary goal is to develop effective and reliable intrusion detection systems capable of accurately identifying intrusions in IoT networks. The research begins by providing an in-depth introduction to the IoT, encompassing its architecture, data flow, components, and application areas. Key challenges faced by the IoT, including security, interoperability, scalability, and energy efficiency, are highlighted. Emphasis is placed on the importance of IoT security, vulnerabilities, and the various types of attacks that can occur. Intrusion Detection Systems (IDS), such as signature-based, anomaly-based, and specification-based IDS, are introduced as essential tools for detecting and mitigating intrusions. The thesis presents the implementation of three deep learning-based intrusion detection programs (DNN, CNN, and RNN) using the PyTorch library, with the effectiveness of these programs evaluated using the IoTID20 dataset. By harnessing deep learning techniques, this research contributes to enhancing the understanding and effectiveness of intrusion detection in IoT networks. Côte titre : MAI/0707 En ligne : https://drive.google.com/file/d/1m2FHecjMZFFPzXSDnF_pmOhCPeKjSQYh/view?usp=drive [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0707	MAI/0707	livre	Bibliothéque des sciences	Anglais	Disponible Disponible

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