半月刊

ISSN 1000-1026

CN 32-1180/TP

+高级检索 English
机器学习在电力信息物理系统网络安全中的应用
作者:
作者单位:

1.浙江大学控制科学与工程学院,浙江省 杭州市 310027;2.贵州大学计算机科学与技术学院,贵州省 贵阳市 550025

摘要:

随着信息化程度的不断深入,传统电力系统逐渐发展成为典型的信息物理系统(CPS)。开放的信息系统环境使得电力系统的安全运行面临着各种潜在网络攻击的威胁。近年来,机器学习方法迅猛发展,并已广泛应用于电力CPS网络安全领域。一方面,电力CPS中数据的爆炸式增长以及硬件运算能力的提升为机器学习的应用创造了良好条件;另一方面,相比于传统的基于机理的建模分析方法,基于数据的机器学习方法具有模型构建以及实时性需求2个方面的优势。文中从攻防2个角度对机器学习在电力CPS网络安全领域的应用进行了归纳总结。其中,攻击者角度主要包括拓扑信息推断、攻击资源优化以及攻击构建3个方面;防守者角度主要包括安全保护、攻击检测以及攻击缓解3个方面。最后,分析展望了电力CPS网络安全领域存在的挑战以及未来的研究方向。

关键词:

基金项目:

国家自然科学基金资助项目(62073285);浙江省自然科学基金重点项目(LZ21F020006)。

通信作者:

作者简介:

彭莎(1998—),女,博士研究生,主要研究方向:智能电网安全、机器学习。E-mail:pengsha_mail123@163.com
孙铭阳(1988—),男,博士,研究员,主要研究方向:人工智能与电力大数据、低碳能源系统优化运行与规划、能源互联网安全。E-mail:mingyangsun@zju.edu.cn
张镇勇(1991—),男,副教授,主要研究方向:工业控制系统安全、智能电网安全、人工智能技术应用安全、移动计算。E-mail: zyzhangnew@gmail.com
邓瑞龙(1987—),男,通信作者,博士,研究员,主要研究方向:工控安全、智能电网、通信网络。E-mail:dengruilong@zju.edu.cn


Application of Machine Learning in Cyber Security of Cyber-Physical Power System
Author:
Affiliation:

1.College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;2.College of Computer Science and Technology, Guizhou University, Guiyang 550025, China

Abstract:

With the deepening of informationization, the traditional power system has been transformed into a typical cyber-physical system (CPS). Considering the open cyber system environment, the security operation of the cyber-physical power system (CPPS) faces threats from various potential cyberattacks. In recent years, machine learning approaches have been developing rapidly and have been widely used in CPPS cyber security. On the one hand, the explosive growth of data in the CPPS and the improvement of hardware computing power create the right conditions for applying machine learning approaches. On the other hand, compared with the traditional model-based approaches, the data-based machine learning approaches has advantages in two aspects: modeling and real-time requirements. This paper summarizes the application of machine learning in CPPS cyber security from the perspectives of attack and defense, respectively. The perspective of attack mainly includes three aspects: topology inference, attacking resource optimization, and attack construction. The perspectives of defense mainly include three aspects: security protection, attack detection, and attack mitigation. Finally, the challenges and future research directions in the field of CPPS cyber security are proposed.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 62073285) and Key Program of Zhejiang Provincial Natural Science Foundation of China (No. LZ21F020006).
引用本文
[1]彭莎,孙铭阳,张镇勇,等.机器学习在电力信息物理系统网络安全中的应用[J].电力系统自动化,2022,46(9):200-215. DOI:10.7500/AEPS20210613001.
PENG Sha, SUN Mingyang, ZHANG Zhenyong, et al. Application of Machine Learning in Cyber Security of Cyber-Physical Power System[J]. Automation of Electric Power Systems, 2022, 46(9):200-215. DOI:10.7500/AEPS20210613001.
复制
支撑数据及附录
分享
历史
  • 收稿日期:2021-06-13
  • 最后修改日期:2021-11-18
  • 录用日期:
  • 在线发布日期: 2022-04-28
  • 出版日期: