CGAAD: Centrality- and Graph-Aware Deep-Learning Model for Detecting Cyberattacks Targeting Industrial Control Systems in Critical Infrastructure-Reference-Cited by-同舟云学术

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CGAAD: Centrality- and Graph-Aware Deep-Learning Model for Detecting Cyberattacks Targeting Industrial Control Systems in Critical Infrastructure

Published:2024-07-01 Issue:13 Volume:11 Page:24162-24182
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Alrumaih Thuraya N. I.¹^ORCID,Alenazi Mohammed J. F.¹^ORCID

Affiliation:

1. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Funder

Researcher Supporting Project, King Saud University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10570342/10504841.pdf?arnumber=10504841

Reference69 articles.

1. Cybersecurity of Industrial Cyber-Physical Systems: A Review

2. An Analysis Study of Security Threats, Vulnerabilities Challenges of Cyber-Physical Systems

3. Stuxnet

4. A Survey of Cyber-Physical Attacks and Detection Methods in Smart Water Distribution Systems

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1. Group Consensus-Driven Energy Consumption Assessment Using Social Network Analysis and Fuzzy Information Fusion;International Journal on Semantic Web and Information Systems;2024-08-16

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