电力网络中基于物理信息的虚假数据入侵检测方法
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摘要
文章针对虚假数据攻击检测方法中难以快速检测的问题,提出一种基于物理信息的虚假数据入侵检测方法。该方法采用高采样的同步相量测量单元实时采集量测数据,计算节点电压稳定性指标(NVSI)。当电网节点中存在异常NVSI值时,采用离群点异常值检测算法找出受攻击的节点;当电网节点中无明显异常NVSI值时,根据节点的NVSI值在时间上变化的差值筛选出脆弱节点,对筛选出的节点使用物理规则协作检测的方法检测出受攻击的节点。文章使用标准IEEE 39节点电力测试系统进行模拟仿真,结果表明,文章方法相比其他方法能较快检测出受攻击的节点,且提高了检测准确率。
Aiming at the difficulty of fast detection in false data intrusion detection method, this paper proposes a false data intrusion detection method based on physical information. The method uses the high-sampling synchronous phasor measurement unit to collect measurement data in real time, and calculates the node voltage stability index(NVSI).When abnormal NVSI values exist in grid nodes, the system is based on the outlier detection algorithm to find the attacked nodes. When there are no obvious abnormal NVSI values in grid nodes, the vulnerable nodes are selected according to the difference of the NVSI value in time, and the attacked nodes are detected by physical rules cooperative detection method for the selected nodes. This paper uses a standard IEEE 39-bus power test system to simulate the system. The results show that the proposed method can detect the attacked nodes faster than other methods, and improve the detection accuracy.
Aiming at the difficulty of fast detection in false data intrusion detection method, this paper proposes a false data intrusion detection method based on physical information. The method uses the high-sampling synchronous phasor measurement unit to collect measurement data in real time, and calculates the node voltage stability index(NVSI).When abnormal NVSI values exist in grid nodes, the system is based on the outlier detection algorithm to find the attacked nodes. When there are no obvious abnormal NVSI values in grid nodes, the vulnerable nodes are selected according to the difference of the NVSI value in time, and the attacked nodes are detected by physical rules cooperative detection method for the selected nodes. This paper uses a standard IEEE 39-bus power test system to simulate the system. The results show that the proposed method can detect the attacked nodes faster than other methods, and improve the detection accuracy.
引文
[1]LIANG Gaoqi,ZHAO Junhua,LUO Fengji,et al.A Review of False Data Injection Attacks Against Modern Power Systems[J].IEEE Transactions on Smart Grid,2017,8(4):1630-1638.
[2]CUI Shuguang,HAN Zhu,KAR S,et al.Coordinated Datainjection Attack and Detection in the Smart Grid:A Detailed Look at Enriching Detection Solutions[J].IEEE Signal Processing Magazine,2012,29(5):106-115.
[3]WANG Yi,AMIN M M,FU Jian,et al.A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids[J].IEEE Access,2017(5):26022-26033.
[4]PAL S,SIKDAR B,CHOW J H.Classification and Detection of PMU Data Manipulation Attacks Using Transmission Line Parameters[J].IEEE Transactions on Smart Grid,2017,9(5):5057-5066.
[5]LIU Yao,NING Peng,REITER M K.False Data Injection Attacks against State Estimation in Electric Power Grids[J].ACMTransactions on Information and System Security,2011,14(1):21-32.
[6]CHEN Poyu Y,YANG Shusen,MCCANN J A,et al.Detection of False Data Injection Attacks in Smart-grid Systems[J].IEEECommunications Magazine,2015,53(2):206-213.
[7]XIA Zhuoqun,ZOU Fengfei,XU Ming,et al.Research on Defensive Strategy of Real-time Price Attack Based on Zerodeterminant[J].Netinfo Security,2017,17(11):25-31.夏卓群,邹逢飞,徐明,等.基于零行列式的实时电价攻击防御策略研究[J].信息网络安全,2017,17(11):25-31.
[8]ESMALIFALAK M,LIU Lanchao,NGUYEN N,et al.Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid[J].IEEE Systems Journal,2017,11(3):1644-1652.
[9]AHMED S,LEE Y D,HYUN S H,et al.Feature SelectionBased Detection of Covert Cyber Deception Assaults in Smart Grid Communications Networks using Machine Learning[J].IEEEAccess,2018(6):27518-27529.
[10]HE Youbiao,MENDIS G J,WEI Jin.Real-time Detection of False Data Injection Attacks in Smart Grid:A Deep Learning-Based Intelligent Mechanism[J].IEEE Transactions on Smart Grid,2017,8(5):2505-2516.
[11]ASHOK A,GOVINDARASU M,AJJARAPU V.Online Detection of Stealthy False Data Injection Attacks in Power System State Estimation[J].IEEE Transactions on Smart Grid,2018,9(3):1636-1646.
[12]GUAN Zhitao,AN P,YANG Tingting.Matrix Partition-based Detection Scheme for False Data Injection in Smart Grid[J].International Journal of Wireless and Mobile Computing,2015,9(3):250-256.
[13]XU Ruzhi,WANG Rui,GUAN Zhitao,et al.Achieving Efficient Detection against False Data Injection Attacks in Smart Grid[J].IEEE Access,2017(5):13787-13798.
[14]LI Beibei,LU Rongxing,WANG Wei,et al.Distributed Hostbased Collaborative Detection for False Data Injection Attacks in Smart Grid Cyber-physical System[J].Journal of Parallel&Distributed Computing,2016,103(C):32-41.
[15]REN Wei,ZHAO Junge.Security Architecture and Analysis of Smart Grid[J].Netinfo Security,2012,12(8):178-181.任伟,赵俊阁.智能电网安全架构与分析[J].信息网络安全,2012,12(8):178-181.
[16]HAZARIKA D.New Method for Monitoring Voltage Stability Condition of a Bus of an Interconnected Power System Using Measurements of the Bus Variables[J].Iet Generation Transmission&Distribution,2012,6(10):977-985.
[17]ZIMMERMAN R D,MURILLO-SANCHEZ C E,THOMAS R J.MATPOWER:Steady-State Operations,Planning,and Analysis Tools for Power Systems Research and Education[J].IEEE Transactions on Power Systems,2011,26(1):12-19.
[2]CUI Shuguang,HAN Zhu,KAR S,et al.Coordinated Datainjection Attack and Detection in the Smart Grid:A Detailed Look at Enriching Detection Solutions[J].IEEE Signal Processing Magazine,2012,29(5):106-115.
[3]WANG Yi,AMIN M M,FU Jian,et al.A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids[J].IEEE Access,2017(5):26022-26033.
[4]PAL S,SIKDAR B,CHOW J H.Classification and Detection of PMU Data Manipulation Attacks Using Transmission Line Parameters[J].IEEE Transactions on Smart Grid,2017,9(5):5057-5066.
[5]LIU Yao,NING Peng,REITER M K.False Data Injection Attacks against State Estimation in Electric Power Grids[J].ACMTransactions on Information and System Security,2011,14(1):21-32.
[6]CHEN Poyu Y,YANG Shusen,MCCANN J A,et al.Detection of False Data Injection Attacks in Smart-grid Systems[J].IEEECommunications Magazine,2015,53(2):206-213.
[7]XIA Zhuoqun,ZOU Fengfei,XU Ming,et al.Research on Defensive Strategy of Real-time Price Attack Based on Zerodeterminant[J].Netinfo Security,2017,17(11):25-31.夏卓群,邹逢飞,徐明,等.基于零行列式的实时电价攻击防御策略研究[J].信息网络安全,2017,17(11):25-31.
[8]ESMALIFALAK M,LIU Lanchao,NGUYEN N,et al.Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid[J].IEEE Systems Journal,2017,11(3):1644-1652.
[9]AHMED S,LEE Y D,HYUN S H,et al.Feature SelectionBased Detection of Covert Cyber Deception Assaults in Smart Grid Communications Networks using Machine Learning[J].IEEEAccess,2018(6):27518-27529.
[10]HE Youbiao,MENDIS G J,WEI Jin.Real-time Detection of False Data Injection Attacks in Smart Grid:A Deep Learning-Based Intelligent Mechanism[J].IEEE Transactions on Smart Grid,2017,8(5):2505-2516.
[11]ASHOK A,GOVINDARASU M,AJJARAPU V.Online Detection of Stealthy False Data Injection Attacks in Power System State Estimation[J].IEEE Transactions on Smart Grid,2018,9(3):1636-1646.
[12]GUAN Zhitao,AN P,YANG Tingting.Matrix Partition-based Detection Scheme for False Data Injection in Smart Grid[J].International Journal of Wireless and Mobile Computing,2015,9(3):250-256.
[13]XU Ruzhi,WANG Rui,GUAN Zhitao,et al.Achieving Efficient Detection against False Data Injection Attacks in Smart Grid[J].IEEE Access,2017(5):13787-13798.
[14]LI Beibei,LU Rongxing,WANG Wei,et al.Distributed Hostbased Collaborative Detection for False Data Injection Attacks in Smart Grid Cyber-physical System[J].Journal of Parallel&Distributed Computing,2016,103(C):32-41.
[15]REN Wei,ZHAO Junge.Security Architecture and Analysis of Smart Grid[J].Netinfo Security,2012,12(8):178-181.任伟,赵俊阁.智能电网安全架构与分析[J].信息网络安全,2012,12(8):178-181.
[16]HAZARIKA D.New Method for Monitoring Voltage Stability Condition of a Bus of an Interconnected Power System Using Measurements of the Bus Variables[J].Iet Generation Transmission&Distribution,2012,6(10):977-985.
[17]ZIMMERMAN R D,MURILLO-SANCHEZ C E,THOMAS R J.MATPOWER:Steady-State Operations,Planning,and Analysis Tools for Power Systems Research and Education[J].IEEE Transactions on Power Systems,2011,26(1):12-19.