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基于贝叶斯序贯博弈模型的智能电网信息物理安全分析

李军 李韬

李军, 李韬. 基于贝叶斯序贯博弈模型的智能电网信息物理安全分析. 自动化学报, 2019, 45(1): 98-109. doi: 10.16383/j.aas.2018.c180336
引用本文: 李军, 李韬. 基于贝叶斯序贯博弈模型的智能电网信息物理安全分析. 自动化学报, 2019, 45(1): 98-109. doi: 10.16383/j.aas.2018.c180336
LI Jun, LI Tao. Cyber-physical Security Analysis of Smart Grids With Bayesian Sequential Game Models. ACTA AUTOMATICA SINICA, 2019, 45(1): 98-109. doi: 10.16383/j.aas.2018.c180336
Citation: LI Jun, LI Tao. Cyber-physical Security Analysis of Smart Grids With Bayesian Sequential Game Models. ACTA AUTOMATICA SINICA, 2019, 45(1): 98-109. doi: 10.16383/j.aas.2018.c180336

基于贝叶斯序贯博弈模型的智能电网信息物理安全分析

doi: 10.16383/j.aas.2018.c180336
基金项目: 

国家自然科学基金 61522310

详细信息
    作者简介:

    李韬 华东师范大学数学科学学院教授.2009年获得中国科学院数学与系统科学院博士学位.主要研究方向为随机系统, 信息, 物理多主体系统, 博弈论.E-mail:tli@math.ecnu.edu.cn

    通讯作者:

    李军 上海大学机电工程与自动化学院博士研究生.主要研究方向为信息物理系统, 智能电网安全, 博弈论.本文通信作者.E-mail:leejun@shu.edu.cn

Cyber-physical Security Analysis of Smart Grids With Bayesian Sequential Game Models

Funds: 

National Natural Science Foundation of China 61522310

More Information
    Author Bio:

    Professor at the School of Mathematical Sciences,East China Normal University.He received his Ph.D.degree from the Academy of Mathematics and Systems Science,Chinese Academy of Sciences in 2009.His research interest covers stochastic systems,cyber-physical multi-agent systems,and game theory

    Corresponding author: LI Jun Ph.D.candidate at the School of Mechatronic Engineering and Automation,Shanghai University.His research interest covers cyber-physical system,smart grid security,and game theory.Corresponding author of this paper
  • 摘要: 智能电网是利用信息技术优化从供应者到消费者的电力传输和配电网络.作为一种信息物理系统(Cyber-physical system,CPS),智能电网由物理设备和负责数据计算与通信的网络组成.智能电网的诸多安全问题会出现在通信网络和物理设备这两个层面,例如注入坏数据和收集客户隐私信息的网络攻击,攻击电网物理设备的物理攻击等.本文主要研究了智能电网的系统管理员(防护者)如何确定攻击者类型,从而选择最优防护策略的问题.提出了一种贝叶斯序贯博弈模型以确定攻击者的类型,根据序贯博弈树得到博弈双方的均衡策略.首先,对类型不确定的攻击者和防护者构建静态贝叶斯博弈模型,通过海萨尼转换将不完全信息博弈转换成完全信息博弈,得到贝叶斯纳什均衡解,进而确定攻击者的类型.其次,考虑攻击者和防护者之间的序贯博弈模型,它能够有效地帮助防护者进行决策分析.通过逆向归纳法分别对两种类型的攻击者和防护者之间的博弈树进行分析,得到博弈树的均衡路径,进而得到攻击者的最优攻击策略和防护者的最优防护策略.分析表明,贝叶斯序贯博弈模型能够使防护者确定攻击者的类型,并且选择最优防护策略,从而为涉及智能电网信息安全的相关研究提供参考.
    1)  本文责任编委 孙秋野
  • 图  1  贝叶斯博弈的扩展式

    Fig.  1  The Bayesian game in an extensive form

    图  2  网络攻击的序贯博弈树

    Fig.  2  The sequential game tree for cyber attacks

    图  3  物理攻击的序贯博弈树

    Fig.  3  The sequential game tree for physical attacks

    表  1  攻击者类型为网络攻击

    Table  1  The type of attacker is a cyber attack

    防护 不防护
    攻击 (1-2α)ω-cic, (2α-1)ω-cd ω-cic; -ω
    不攻击 0, -βω-cd 0, 0
    下载: 导出CSV

    表  2  攻击者类型为物理攻击

    Table  2  The type of attacker is a physical attack

    防护 不防护
    攻击 (1-2α)ω-cip, (2α-1)ω-cd ω-cip; -ω
    不攻击 0, -βω-cd 0, 0
    下载: 导出CSV

    表  3  行为函数收益

    Table  3  The payoff of the behavioral function

    $A(S, a, d)$ $a$为攻击者策略 $a$为防护者策略
    $S$为攻击者 d×Impact(a) 0
    $S$为防护者 $ - Impact(a)^{d}$ d×Impact(a)
    下载: 导出CSV

    表  4  行为策略$a$的影响函数(网络攻击)

    Table  4  The payoff of the behavioral function

    行为策略$(a)$ $C(a)$ $I(a)$ $A(a)$ $SF(a)$ $Impact(a)$
    $d_{\langle km, jd\rangle}$ $m$ $m$ $l$ $h$ $0.3l + 0.6m + 0.1h$
    $a_{ce}$ $h$ $l$ $l$ $m$ $0.7l + 0.1m + 0.2h$
    $a_{cj}$ $l$ $h$ $m$ $l$ $0.3l + 0.3m + 0.4h$
    $a_{cd}$ $l$ $h$ $m$ $h$ $0.2l + 0.3m + 0.5h$
    下载: 导出CSV

    表  5  行为策略$a$的影响函数(物理攻击)

    Table  5  The payoff of the behavioral function (physical attack)

    行为策略$(a)$ $C(a)$ $I(a)$ $A(a)$ $SF(a)$ $Impact(a)$
    $d_{\langle ca, mp\rangle}$ $l$ $m$ $m$ $m$ $0.1l + 0.9m$
    $a_{ps}$ $m$ $l$ $l$ $m$ $0.8l + 0.2m$
    $a_{pn}$ $l$ $m$ $m$ $m$ $0.1l + 0.9m$
    $a_{pt}$ $l$ $h$ $h$ $h$ $0.1l + 0.9h$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-29
  • 录用日期:  2018-09-14
  • 刊出日期:  2019-01-20

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