Detection of False Data Injection Attacks in Cyber-Physical Power Systems: An Adaptive Adversarial Dual Autoencoder With Graph Representation Learning Approach-Reference-Cited by-同舟云学术

Detection of False Data Injection Attacks in Cyber-Physical Power Systems: An Adaptive Adversarial Dual Autoencoder With Graph Representation Learning Approach

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Feng Hantong¹^ORCID,Han Yinghua¹^ORCID,Si Fangyuan²^ORCID,Zhao Qiang³^ORCID

Affiliation:

1. School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China

2. School of Electrical Engineering, Beijing Jiaotong University, Beijing, China

3. School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Natural Science Foundation of Hebei Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10314423.pdf?arnumber=10314423

Reference46 articles.

1. Industrial process modeling and monitoring based on jointly specific and shared dictionary learning

2. Power Measurement-Based Vulnerability Assessment of IoT Medical Devices at Varying Countermeasures for Cybersecurity

3. A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

4. Performance Analysis of Electricity Theft Detection for the Smart Grid: An Overview

5. Distributed Estimation of Power System Oscillation Modes Under Attacks on GPS Clocks

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