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摘要: 随着电力信息通信技术的发展与应用，电力流与信息流深度融合，共同实现对系统的全景状态感知与控制决策，电力系统转变成典型的信息物理系统（Cyber physical system，CPS）.开放的通信环境与复杂的信息物理耦合交互过程，使得信息安全风险成为影响电力系统安全稳定运行的重要因素.其中，虚假数据注入攻击（False data injection attack，FDIA）通过破坏网络数据完整性以干扰控制决策，是一种典型的网络攻击方式.本文针对面向电力CPS的虚假数据注入的攻击过程和防御手段进行了分析与总结.从攻击者视角分析了FDIA的攻击目标、策略及后果；从防御者视角总结了保护与检测环节中的各类方法；最后基于联合仿真技术，提出了针对虚假数据攻防过程建模和评估的电力CPS联合攻防平台.Abstract: With the development and application of information communication technology, the power flow and the information flow are becoming deeply integrated in order to achieve panoramic state awareness and control decision. Therefore the power system has been transformed into a typical cyber-physical system (CPS). Considering the open communication environment and complex cyber physical coupling mechanism, the information risk has become an important issue posing severe threats to the secure operation of power systems. False data injection attack (FDIA), as a typical attack mode destroying data integrity, can interfere with the control decision. In this paper, the attack process and defense method of false data injection are analyzed and summarized. Firstly, from the perspective of attackers, the goals, strategies and consequences of FDIA are comprehensively discussed. Then, from the perspective of defenders, the protection and detection countermeasures are discussed. Finally the co-simulation technology is utilized to construct associated attack-defense platform of cyber-physical power system (CPPS), which is aimed at modeling and assessment of the attack and defense processes of false data injection.1) 本文责任编委 孙秋野
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图 4 电力CPS数据流中的FDIA攻防手段概括
Fig. 4 The attack-defense means of FDIA aimed at the data flow in CPPS
图 5 电力CPS网络联合攻防平台框架
Fig. 5 The framework of the CPPS associated attack-defense platform
表 1 电力CPS网络安全攻防平台研究现状
Table 1 Researches of CPPS associated attack-defense platforms
组成结构 攻击场景 防御方法 验证效果 MATLAB, OPNET[60] 信息传输故障 基于多维尺度和局部异常因子的检测方法 实现CPS各区域故障的统一识别与定位 Digsilent, OMNET++, MATLAB[61] 针对EMS的FDIA 检测和事后恢复方法 构建了脆弱性和攻击影响的综合评估框架 PSLF, NS2[62] 针对PMU状态估计器的FDIA 基于状态变量时效性的检测方法 验证了状态估计器对单链路和路由器故障、拥塞的鲁棒性 MATLAB, OPNET, C++[63] 针对广域阻尼控制系统的拒绝服务、中间人等攻击 无 分析归纳了延迟、无序、错误数据的端到端信息特征 Simulink, OPNET[64] 目的为延迟和污染通信数据的分布式拒绝服务攻击 无 观察了网络攻击对故障附近发电机响应的影响 RTDS, Python, AutoIt[65] 面向广域监控系统应用的电力CPS突发事件和网络攻击行为 基于数据挖掘的入侵异常检测方法 验证了电力系统应对攻击的脆弱性评估、异常检测与故障检测能力 
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