The Coordinated Cyber Physical Power Attack Strategy Based on Worm Propagation and False Data Injection
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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, 能够更有效的识别系统潜在风险. 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.Abstract: With the deep integration of information technologies in modern power systems, cyber system anomalies and network attacks can threaten the safety and stability of power system operation. To study the security risks of the power system caused by the latest industrial control worm, a coordinated cyber-physical power attack model based on Markov Decision Process (MDP) is proposed in this thesis. Then, the Q-learning algorithm is adopted to search for the optimal attack strategy in the proposed model, and the error of state estimation result induced by the attacks is devised to quantify the potential physical influences-attack benefits. Eventually, numerical joint simulation experiments are conducted on the 8CYBER_NODE-14BUS coupling test system, and the results show that the coordinated attack model proposed in this paper is more destructive. Compared with the traditional isolated physical attack without considering the cyber network, the identified weak nodes can also consider the influence of the cyber devices and guide the allocation of defense resources.
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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 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 7 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 
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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 C-n 1 C-n 2 C-n 3 C-n 4 C-n 5 C-n 6 C-n 7 C-n 8 节点耦合 Bus 2 Bus 4 Bus 6 Bus 7 Bus 8 Bus 10 Bus 13 Bus 14 协同攻击 31.65 32.51 30.60 0.67 0.85 1.00 1.44 1.25 物理攻击 16.66 16.40 11.27 15.26 5.97 19.54 8.70 6.20
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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 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(%) 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
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表 4 NS2中通信网络的参数配置
Table 4 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
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表 5 每个通信设备上存在的漏洞的CVSS评分
Table 5 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
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