Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques-Reference-Cited by-同舟云学术

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Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques

Published:2024-07 Issue: Volume:26 Page:101149
ISSN:2542-6605
Container-title:Internet of Things
language:en
Short-container-title:Internet of Things

Author:

Hamouda Djallel,Ferrag Mohamed Amine^ORCID,Benhamida Nadjette,Seridi Hamid,Ghanem Mohamed Chahine^ORCID

Publisher

Elsevier BV

Reference37 articles.

1. Security of smart manufacturing systems;Tuptuk;J. Manuf. Syst.,2018

2. Cybersecurity for industry 4.0 in the current literature: A reference framework;Lezzi;Comput. Ind.,2018

3. Attack-resilient control against FDI attacks in cyber-physical systems;Chen;IEEE/CAA J. Autom. Sin.,2022

4. Generative adversarial networks: Introduction and outlook;Wang;IEEE/CAA J. Autom. Sin.,2017

5. Intrusion detection system for PS-poll DoS attack in 802.11 networks using real time discrete event system;Agarwal;IEEE/CAA J. Autom. Sin.,2016

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Generative AI for Threat Hunting and Behaviour Analysis;Advances in Digital Crime, Forensics, and Cyber Terrorism;2024-09-13

2. Generative AI for Threat Intelligence and Information Sharing;Advances in Digital Crime, Forensics, and Cyber Terrorism;2024-09-13

3. Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods;Journal of Intelligent Systems;2024-01-01

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