A Generalizable Deep Neural Network Method for Detecting Attacks in Industrial Cyber-Physical Systems-Reference-Cited by-同舟云学术

A Generalizable Deep Neural Network Method for Detecting Attacks in Industrial Cyber-Physical Systems

Published:2023 Issue: Volume: Page:1-9
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Sakhnini Jacob¹,Karimipour Hadis²^ORCID,Dehghantanha Ali³^ORCID,Yazdinejad Abbas³^ORCID,Gadekallu Thippa Reddy⁴^ORCID,Victor Nancy⁵,Islam Anik²^ORCID

Affiliation:

1. School of Engineering, University of Guelph, Guelph, ON, Canada

2. Schulich School of Engineering, University of Calgary, Calgary, Canada

3. School of Computer Science, University of Guelph, Guelph, ON, Canada

4. Zhongda Group, Haiyan County, Jiaxing City, Zhejiang Province, China

5. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/4357939/10163759.pdf?arnumber=10163759

Reference36 articles.

1. A Survey of Cyber-Physical Systems From a Game-Theoretic Perspective

2. Relaxation‐based anomaly detection in cyber‐physical systems using ensemble kalman filter

3. Cybersecurity of Smart Inverters in the Smart Grid: A Survey

4. DG dynamic voltage support requirements for the reliable protection of reconfigurable networks

5. Security of Wide-Area Monitoring, Protection, and Control (WAMPAC) Systems of the Smart Grid: A Survey on Challenges and Opportunities

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