Attack Detection in IoT-Based Healthcare Networks Using Hybrid Federated Learning-Reference-Cited by-同舟云学术

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Attack Detection in IoT-Based Healthcare Networks Using Hybrid Federated Learning

Published:2023-07-25 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Smart Applications, Communications and Networking (SmartNets)
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Author:

Itani May¹,Basheer Hanaa²,Eddine Fouad³

Affiliation:

1. Beirut Arab University,Department of Computer Science,Beirut,Lebanon,1101-2809

2. University of Baghdad,Photonics Unit Laser Institute,Baghdad,Iraq,10001

3. Security Operation Center, Monty Mobile,AbdulKhaleq Information Security Department,Beirut,Lebanon,1003

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10215379/10215514/10216144.pdf?arnumber=10216144

Reference20 articles.

1. Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study

2. Deep Learning-Based Intrusion Detection for IoT Networks

3. Data augmentation for attack detection on iot telehealth systems;khan;Ph D Dissertation,2022

4. Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

5. Federated Learning-Inspired Technique for Attack Classification in IoT Networks

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1. Scouting the Juncture of Internet of Things (IoT), Deep Learning, and Cybercrime;Advances in Digital Crime, Forensics, and Cyber Terrorism;2024-09-13

2. Harnessing Machine Learning Intelligence Against Cyber Threats;Advances in Business Strategy and Competitive Advantage;2024-08-28

3. A hybrid intrusion detection approach based on message queuing telemetry transport (MQTT) protocol in industrial internet of things;Transactions on Emerging Telecommunications Technologies;2024-08-20

4. Machine Learning Algorithms for the Detection of Threats in IoT Healthcare;2023 International Conference on Emerging Research in Computational Science (ICERCS);2023-12-07

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