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2024 , Vol. 42 >Issue 9: 6 - 16

DOI: https://doi.org/10.3981/j.issn.1000-7857.2023.11.01692

专稿

智能电网的网络安全风险及应对策略

  • 岳芳 ,
  • 王雪珍 ,
  • 姜山
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  • 1. 中国科学院武汉文献情报中心, 武汉 430071;
    2. 科技大数据湖北省重点实验室, 武汉 430071;
    3. 中国科学院宁波材料技术与工程研究所, 宁波 315201;
    4. 甬江实验室, 宁波 315202
岳芳，副研究员，研究方向为能源科技战略情报，电子信箱：yuef@whlib.ac.cn

收稿日期: 2023-11-13

  修回日期: 2024-03-15

  网络出版日期: 2024-06-12

基金资助

中国科学院A类战略性先导科技专项(XDA29010500);中国科学院战略研究与决策支持系统建设专项(GHJ-ZLZX-2023-06)

收起

Network security risks and new countermeasures under the smart grid environment

  • YUE Fang ,
  • WANG Xuezhen ,
  • JIANG Shan
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  • 1. National Science Library (Wuhan), Chinese Academy of Sciences, Wuhan 430071, China;
    2. Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071, China;
    3. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China;
    4. Yongjiang Laboratory, Ningbo 315202, China

Received date: 2023-11-13

  Revised date: 2024-03-15

  Online published: 2024-06-12

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摘要

智能电网将网络和信息技术与电网融合以增强电力系统的可靠性、安全性和效率，但在高度信息化和互联环境下，智能电网面临着日益复杂多变的网络安全风险。概述了智能电网的概念和架构，指出其双向可交互性是与传统电网最大的区别。总结了智能电网的安全漏洞和面临的网络攻击，主要分为机密性攻击、完整性攻击和可用性攻击3类。总结回顾了机器学习、区块链、量子计算等增强智能电网网络安全的新策略，机器学习算法可增强电网故障检测和攻击识别的准确性和灵敏度，区块链技术通过其去中心化和防篡改特性为智能电网提供身份验证、数据安全、隐私保护等解决方案，量子计算在电网故障诊断和数据传输安全方面有巨大应用潜力。提出了未来的主要挑战和研究方向。

关键词： 智能电网; 网络攻击; 机器学习; 区块链; 量子计算

本文引用格式

岳芳 , 王雪珍 , 姜山 . 智能电网的网络安全风险及应对策略[J]. 科技导报, 2024 , 42(9) : 6 -16 . DOI: 10.3981/j.issn.1000-7857.2023.11.01692

Abstract

The smart grid integrates network and information technologies with the grid to enhance the reliability, security, and efficiency of the power system. However, in a highly digitalized and interconnected environment, the smart grid faces increasingly complex and variable network security risks. This paper outlines the concept and architecture of the smart grid, indicating that its biggest difference from the traditional grid is bidirectional interactivity. Then, the security vulnerabilities and cyber attacks are summarized in terms of three categories, namely confidentiality attacks, integrity attacks, and availability attacks. Then, new strategies for enhancing the network security of smart grid, such as machine learning, blockchain, quantum computing are reviewed. Machine learning algorithms can improve the accuracy and sensitivity of power grid fault detection and attack identification. Blockchain technology provides solutions for identity verification, data security, and privacy protection through its decentralized and tamper-resistant features. Quantum computing has significant application potential in power grid fault diagnosis and data transmission security. Finally, the major challenges and future research directions are prsented.

Key words： smart grid; cyber attacks; machine learning; blockchain; quantum computing

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