A Blended Deep Learning Intrusion Detection Framework for Consumable Edge-Centric IoMT Industry-Reference-Cited by-同舟云学术

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A Blended Deep Learning Intrusion Detection Framework for Consumable Edge-Centric IoMT Industry

Published:2024-02 Issue:1 Volume:70 Page:2049-2057
ISSN:0098-3063
Container-title:IEEE Transactions on Consumer Electronics
language:
Short-container-title:IEEE Trans. Consumer Electron.

Author:

Alzubi Jafar A.¹^ORCID,Alzubi Omar A.²^ORCID,Qiqieh Issa¹^ORCID,Singh Ashish³^ORCID

Affiliation:

1. Faculty of Engineering, Al-Balqa Applied University, Al-Salt, Jordan

2. Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan

3. Department of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, India

Funder

Deanship of Scientific Research and Innovation at Al-Balqa Applied University, Al-Salt, Jordan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/30/10510045/10382176.pdf?arnumber=10382176

Reference52 articles.

1. An Intelligent Intrusion Detection System for Smart Consumer Electronics Network

2. Anomaly Detection in Smart Home Operation From User Behaviors and Home Conditions

3. An Explainable and Resilient Intrusion Detection System for Industry 5.0

4. A low-rank learning based Multi-Label Security Solution for Industry 5.0 Consumers using Machine Learning Classifiers

5. Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey

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1. Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability;Intelligent Systems with Applications;2024-09

2. An IoT Intrusion Detection Approach Based on Salp Swarm and Artificial Neural Network;International Journal of Network Management;2024-08-18

3. Meta-IDS: Meta-Learning-Based Smart Intrusion Detection System for Internet of Medical Things (IoMT) Network;IEEE Internet of Things Journal;2024-07-01

4. End-to-End Network Intrusion Detection Based on Contrastive Learning;Sensors;2024-03-26

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