FLLF: A Fast-Lightweight Location Detection Framework for False Data Injection Attacks in Smart Grids-Reference-Cited by-同舟云学术

FLLF: A Fast-Lightweight Location Detection Framework for False Data Injection Attacks in Smart Grids

Published:2024-01 Issue:1 Volume:15 Page:911-920
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Zhu Jianxin¹,Meng Wenchao²^ORCID,Sun Mingyang¹^ORCID,Yang Jun¹^ORCID,Song Zhuo³

Affiliation:

1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China

2. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, and the Key Laboratory of CS&AUS of Zhejiang Province, Zhejiang University, Hangzhou, China

3. Basic Software Team, Alibaba Cloud, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Alibaba–Zhejiang University Joint Research Institute of Frontier Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/10372481/10121794.pdf?arnumber=10121794

Reference29 articles.

1. A Fast Locational Detection Model for False Data Injection Attack Based on Edge Computing

2. Robust Data-Driven State Estimation for Smart Grid

3. Detecting false data injection attacks on DC state estimation;Bobba

4. Defending Against False Data Injection Attacks on Power System State Estimation

5. A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Markov game based on reinforcement learning solution against cyber–physical attacks in smart grid;Expert Systems with Applications;2024-12

2. FDIA localization and classification detection in smart grids using multi-modal data and deep learning technique;Computers and Electrical Engineering;2024-11

3. Practical Privacy-Preserving Electricity Theft Detection for Smart Grid;IEEE Transactions on Smart Grid;2024-07

4. Efficient HW/SW Co-design of FPGA Accelerator to Detect Anomaly Attacks in Smart Grids;2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE);2024-06-18

5. Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Machine Learning;Electronics;2024-06-08