A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection-Reference-Cited by-同舟云学术

A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection

Published:2024 Issue: Volume:12 Page:45762-45772
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Alshehri Mohammed S.¹^ORCID,Saidani Oumaima²^ORCID,Alrayes Fatma S.²^ORCID,Abbasi Saadullah Farooq³^ORCID,Ahmad Jawad⁴^ORCID

Affiliation:

1. Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia

2. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia

3. Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, U.K.

4. School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, U.K.

Funder

Deanship of Scientific Research at Najran University

Princess Nourah bint Abdulrahman University Researchers

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10477980.pdf?arnumber=10477980

Reference35 articles.

1. Deep-IFS: Intrusion Detection Approach for Industrial Internet of Things Traffic in Fog Environment

2. An explainable efficient flow-based Industrial IoT intrusion detection system

3. 2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT

4. Intrusion Detection based on Privacy-preserving Federated Learning for the Industrial IoT

5. Multi-step attack detection in industrial networks using a hybrid deep learning architecture

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