False Data Injection Attack Detection Based on Local Linear Embedding and Extreme Learning Machine-Reference-Cited by-同舟云学术

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False Data Injection Attack Detection Based on Local Linear Embedding and Extreme Learning Machine

Published:2022-06-27 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 17th International Conference on Control & Automation (ICCA)
language:
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Author:

Dai Xueying¹,Yi Xinwei¹,Zhou Dan¹,Guo Fanghong¹,Liu Dong²

Affiliation:

1. Zhejiang University of Technology,Department of Automation,Hangzhou,China,310023

2. Candela New Energy Technology (Yangzhou) Co., Ltd.,Yangzhou,China,225000

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9831427/9831807/09831851.pdf?arnumber=9831851

Reference20 articles.

1. An Efficient Data-Driven False Data Injection Attack in Smart Grids

2. Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis

3. Efficient detection of false data injection attack with invertible automatic encoder and long‐short‐term memory

4. False data injection against state estimation in power systems with multiple cooperative attackers

5. Nonlinear Dimensionality Reduction by Locally Linear Embedding

Cited by 2 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. Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A survey;Expert Systems with Applications;2024-11

2. Maximizing intrusion detection efficiency for IoT networks using extreme learning machine;Discover Internet of Things;2024-07-09

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