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基于蠕虫传播和FDI的电力信息物理协同攻击策略

冯晓萌 孙秋野 王冰玉 高嘉文

冯晓萌, 孙秋野, 王冰玉, 高嘉文. 基于蠕虫传播和FDI的电力信息物理协同攻击策略. 自动化学报, 2022, 48(10): 2429−2441 doi: 10.16383/j.aas.c190574
引用本文: 冯晓萌, 孙秋野, 王冰玉, 高嘉文. 基于蠕虫传播和FDI的电力信息物理协同攻击策略. 自动化学报, 2022, 48(10): 2429−2441 doi: 10.16383/j.aas.c190574
Feng Xiao-Meng, Sun Qiu-Ye, Wang Bing-Yu, Gao Jia-Wen. The coordinated cyber physical power attack strategy based on worm propagation and false data injection. Acta Automatica Sinica, 2022, 48(10): 2429−2441 doi: 10.16383/j.aas.c190574
Citation: Feng Xiao-Meng, Sun Qiu-Ye, Wang Bing-Yu, Gao Jia-Wen. The coordinated cyber physical power attack strategy based on worm propagation and false data injection. Acta Automatica Sinica, 2022, 48(10): 2429−2441 doi: 10.16383/j.aas.c190574

基于蠕虫传播和FDI的电力信息物理协同攻击策略

doi: 10.16383/j.aas.c190574
基金项目: 国家自然科学基金重点项目(61433004), 国家自然科学基金(61573094)资助
详细信息
    作者简介:

    冯晓萌:东北大学信息科学与工程学院硕士研究生. 主要研究方向为电力信息物理系统建模及安全防御.E-mail: fengxiaomeng12345@outlook.com

    孙秋野:东北大学信息科学与工程学院教授. 主要研究方向为网络控制技术, 分布式控制技术, 分布式优化分析及其在能源互联网、微网、配电网等领域相关应用. 本文通信作者.E-mail: sunqiuye@mail.neu.edu.cn

    王冰玉:东北大学信息科学与工程学院博士研究生. 主要研究方向为信息物理能源系统, 微电网控制和多智能体系统.E-mail: 1610266@stu.neu.edu.cn

    高嘉文:东北大学信息科学与工程学院硕士研究生. 主要研究方向为电力信息物理系统建模及安全防御.E-mail: helensun0708@outlook.com

The Coordinated Cyber Physical Power Attack Strategy Based on Worm Propagation and False Data Injection

Funds: Supported by Key Program of National Natural Science Founda-tion of China (61433004) and National Natural Science Foundation of China (61573094)
More Information
    Author Bio:

    FENG Xiao-Meng Master student at the School of Information Science and Engineering, Northeastern University. Her research interest covers cyber security for cyber-physical power system

    SUN Qiu-Ye Professor at the School of Information Science and Engineering, Northeastern University. His research interest covers network control technology, distributed control technology, distributed optimization analysis and various applications in energy internet, microgrid, and power distribution network. Corresponding author of this paper

    WANG Bing-Yu Ph.D. candidate at the School of Information Science and Engineering, Northeastern University. Her research interest covers cyber-physical energy system, control strategy of microgrid, and multiagent systems

    GAO Jia-Wen Master student at the School of Information Science and Engineering, Northeastern University. His research interest covers cyber security for cyber-physical power system

  • 摘要: 随着信息技术与现代电力系统的结合日趋紧密, 通信系统异常和网络攻击均可能影响到电力系统的安全稳定运行. 为了研究工控蠕虫病毒对电网带来的安全隐患, 本文首次建立了基于马尔科夫决策过程(Markov decision process, MDP)的电力信息物理系统跨空间协同攻击模型, 该模型同时考虑通信设备漏洞被利用的难易程度为代价以及对电力网络的破坏程度为收益两方面因素, 能够更有效地识别系统潜在风险. 其次, 采用Q学习算法求解在该模型下的最优攻击策略, 并依据电力系统状态估计的误差值来评定该攻击行为对电力系统造成的破坏程度. 最后, 本文在通信8节点−电力14节点的耦合系统上进行联合仿真, 对比结果表明相较单一攻击方式, 协同攻击对电网的破坏程度更大. 与传统的不考虑通信网络的电力层攻击研究相比, 本模型辨识出的薄弱节点也考虑了信息层的关键节点的影响, 对防御资源的分配有指导作用.
  • 图  1  电力信息物理协同攻击示意图

    Fig.  1  Diagram of electrical cyber-physical cooperative attacks

    图  2  通信网络的SIR蠕虫扩散模型状态转换图

    Fig.  2  SIR worm diffusion model state transition diagram of the cyber network

    图  3  电力信息物理耦合网络

    Fig.  3  The network of cyber-physical power coupling system

    图  4  信息物理协同攻击下跨空间渗透和反馈决策机理

    Fig.  4  Cross-space penetration and feedback decision mechanism under cyber-physical collaborative attack

    图  5  通信8节点−电力IEEE14节点耦合系统

    Fig.  5  Cyber 8-Power IEEE14 node coupling system

    图  6  每个训练周期的累积收益

    Fig.  6  Accumulated benefit for each trial

    图  7  最优攻击策略下攻击者的攻击序列和病毒扩散序列

    Fig.  7  The attack sequence and virus spreading sequence under the optimal attack strategy

    图  8  在最优攻击策略下电压幅值差百分比

    Fig.  8  Difference percentage in voltage amplitude under optimal attack strategy

    图  9  注入虚假数据取不同符号下电力设备被攻击的可能性分析

    Fig.  9  The vulnerability analysis of power equipment under different signs of false data

    表  1  考虑不同攻击方法下的影响

    Table  1  Attack effect under different attack methods

    攻击类型 参数 n = 1 n = 2 n = 3
    网络攻击 $\pi^*$ 1 $2\rightarrow 3$ $2\rightarrow 3\rightarrow 4$
    $f(\Delta \theta )$ 0.022 0.103 0.2333
    $f(\Delta V )$ 0.043 0.115 0.245
    物理攻击 $\pi^*$ 4 $5 \rightarrow 6$ $5\rightarrow 4\rightarrow 7$
    $f(\Delta \theta )$ 0.035 0.144 0.344
    $f(\Delta V )$ 0.061 0.134 0.444
    协同攻击 $\pi^*$ 3 $6 \rightarrow 7$ $2 \rightarrow 4 \rightarrow 8$
    $f(\Delta \theta )$ 0.077 0.223 0.523
    $f(\Delta V )$ 0.062 0.267 0.667
    下载: 导出CSV

    表  2  电力设备被攻击可能性分析(%)

    Table  2  The vulnerability analysis of power equipment (%)

    通信−电力 节点耦合 协同攻击 物理攻击
    C-n 1 Bus 2 31.65 16.66
    C-n 2 Bus 4 32.51 16.40
    C-n 3 Bus 6 30.60 11.27
    C-n 4 Bus 7 0.67 15.26
    C-n 5 Bus 8 0.85 5.97
    C-n 6 Bus 10 1.00 19.54
    C-n 7 Bus 13 1.44 8.70
    C-n 8 Bus 14 1.25 6.20
    下载: 导出CSV

    表  3  系统离散程度不同时电力设备被攻击的可能性分析

    Table  3  The vulnerability analysis of power equipment under different discrete degrees of false data

    离散状态数目 各个电力设备被攻击的可能性分析 (%)
    母线标号 Bus 2 Bus 4 Bus 6 Bus 7 Bus 8 Bus 10 Bus 13 Bus 14
    $N_V^g = N_\theta^g = 4$ 7.18 20.88 13.36 18.25 6.54 16.03 9.02 6.31
    $N_V^g = N_\theta^g = 6$ 8.31 19.95 12.97 17.66 6.43 17.38 10.50 6.80
    $N_V^g = N_\theta^g = 8$ 8.11 20.45 12.27 17.66 6.97 17.54 9.70 7.20
    下载: 导出CSV

    A1  NS2中通信网络的参数配置

    A1  The parameters of cyber network in NS2

    起点 终点 带宽 (Mbps) 时延 (ms)
    C-n 1 C-n 2 60 60
    C-n 2 C-n 6 60 20
    C-n 2 C-n 8 60 20
    C-n 7 C-n 8 60 20
    C-n 7 C-n 6 60 20
    C-n 1 C-n 3 60 60
    C-n 3 C-n 4 60 20
    C-n 3 C-n 5 60 20
    C-n 4 C-n 5 60 20
    下载: 导出CSV

    A2  每个通信设备上存在的漏洞的CVSS评分

    A2  The CVSS standards of each cyber node

    标号 漏洞 ID 标号 影响度量分数 漏洞利用分数 基础分数
    C-n 1 CVE-2016-8366 3.4 3.9 7.3
    C-n 2 CVE-2016-8366 3.4 3.9 7.3
    C-n 3 CVE-2016-8366 3.4 3.9 7.3
    C-n 4 CVE-2017-14470 2.7 2.8 5.5
    C-n 5 CVE-2017-14470 2.7 2.8 5.5
    C-n 6 CVE-2017-14470 2.7 2.8 5.5
    C-n 7 CVE-2018-16210 5.9 3.9 9.8
    C-n 8 CVE-2018-16210 5.9 3.9 9.8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-08-09
  • 录用日期:  2020-04-07
  • 网络出版日期:  2022-09-20
  • 刊出日期:  2022-10-14

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