EIDM: deep learning model for IoT intrusion detection systems

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource, 1 online resource.

Language: Englisch

Bibliographic citation: EIDM: deep learning model for IoT intrusion detection systems ; volume:79 ; number:12 ; day:22 ; month:3 ; year:2023 ; pages:13241-13261 ; date:8.2023
The journal of supercomputing ; 79, Heft 12 (22.3.2023), 13241-13261, 8.2023

Creator: Elnakib, Omar
Shaaban, Eman
Mahmoud, Mohamed
Emara, Karim

Contributor: SpringerLink (Online service)

DOI: 10.1007/s11227-023-05197-0

URN: urn:nbn:de:101:1-2023092209325042435485

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:54 AM CEST

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Elnakib, Omar
Shaaban, Eman
Mahmoud, Mohamed
Emara, Karim
SpringerLink (Online service)

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Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset

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EIDM: deep learning model for IoT intrusion detection systems

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Other Objects (12)

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Towards adversarial realism and robust learning for IoT intrusion detection and classification

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Intelligent intrusion detection framework for multi-clouds – IoT environment using swarm-based deep learning classifier

eBF: an enhanced Bloom Filter for intrusion detection in IoT

Maximizing intrusion detection efficiency for IoT networks using extreme learning machine

Autonomous intrusion detection for IoT: a decentralized and privacy preserving approach

Towards adversarial realism and robust learning for IoT intrusion detection and classification

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Enhancing network intrusion detection systems with combined network and host traffic features using deep learning: deep learning and IoT perspective

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Anomaly-based intrusion detection system for IoT application

A novel intrusion detection framework for optimizing IoT security

Intelligent intrusion detection framework for multi-clouds – IoT environment using swarm-based deep learning classifier

eBF: an enhanced Bloom Filter for intrusion detection in IoT

Maximizing intrusion detection efficiency for IoT networks using extreme learning machine

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Towards adversarial realism and robust learning for IoT intrusion detection and classification

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Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning

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Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset

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