LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection-Reference-Cited by-同舟云学术

LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection

Published:2024-09 Issue:5 Volume:21 Page:4418-4432
ISSN:1545-5971
Container-title:IEEE Transactions on Dependable and Secure Computing
language:
Short-container-title:IEEE Trans. Dependable and Secure Comput.

Author:

Tian Jiwei¹^ORCID,Shen Chao¹^ORCID,Wang Buhong²^ORCID,Xia Xiaofang³^ORCID,Zhang Meng¹^ORCID,Lin Chenhao¹^ORCID,Li Qian¹^ORCID

Affiliation:

1. Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China

2. Information and Navigation College, Air Force Engineering University, Xi’an, China

3. School of Computer Science and Technology, Xidian University, Xi’an, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Shaanxi Province Key Industry Innovation Program

Natural Science Foundation of Sichuan Province

China Postdoctoral Science Foundation

Shaanxi Province Postdoctoral Research Funding

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8858/10663874/10398418.pdf?arnumber=10398418

Reference63 articles.

1. Impact of Stealthy Attacks on Optimal Power Flow: A Simulink-Driven Formal Analysis

2. Voltage Stability Constrained Moving Target Defense Against Net Load Redistribution Attacks

3. PDDL: Proactive Distributed Detection and Localization Against Stealthy Deception Attacks in DC Microgrids

4. A Highly Discriminative Detector Against False Data Injection Attacks in AC State Estimation

5. Generative-Adversarial Class-Imbalance Learning for Classifying Cyber-Attacks and Faults - A Cyber-Physical Power System

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