Enhancing Network Security: Machine Learning Evaluation for Intrusion Detection in Power Load Management System-Reference-Cited by-同舟云学术

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Enhancing Network Security: Machine Learning Evaluation for Intrusion Detection in Power Load Management System

Published:2024-05-31 Issue: Volume: Page:708-712
ISSN:
Container-title:2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)
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Author:

Li Bin¹,Zhong Jiyuan¹,Wang Shuyang²,Yu Haoyang³,Guo Qiang⁴,Ren Mingyuan⁵,Chen Ke⁶

Affiliation:

1. North China Electric Power University,School of Electric and Electronic Engineering,Beijing,China

2. China Electric Power Research Institute,Beijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique,Beijing,China

3. State Grid Yichun Electric Power Supply Company,China

4. State Grid Changyi Electric Power Supply Company,China

5. State Grid Gansu Electric Power Company,China

6. China Electric Power Research,Beijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10667475/10667476/10667483.pdf?arnumber=10667483

Reference12 articles.

1. Machine Learning Based Intrusion Detection System for Real-Time Smart Grid Security

2. Kavach: A Machine Learning based approach for enhancing the attack detection capability of firewalls

3. Group Signature Based Federated Learning in Smart Grids

4. Construction of Intelligent Power Grid Load Management System Based on Artificial Neural Network Algorithm

5. Intelligent Smart Power Grid Intrusion Detection System Using Preprocessing and Classification Techniques

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