Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by de...Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.展开更多
配电变压器三相不平衡运行异常监测容易受到外界噪声干扰,导致监测结果出现偏差,因此提出结合虚假数据注入攻击(False Data Injection Attack,FDIA)检测模型和指数加权移动平均的异常监测方法。通过分布式FDIA检测模型,提取三相不平衡...配电变压器三相不平衡运行异常监测容易受到外界噪声干扰,导致监测结果出现偏差,因此提出结合虚假数据注入攻击(False Data Injection Attack,FDIA)检测模型和指数加权移动平均的异常监测方法。通过分布式FDIA检测模型,提取三相不平衡运行异常数据。利用指数加权移动平均方法,平滑处理误差。以滑动窗口的均值为中心,分析运行异常情况,计算三相电压不平衡度,实现三相不平衡运行异常监测。实验结果表明,该方法的监测准确性较高,为电力系统的安全稳定运行提供有力保障。展开更多
False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unr...False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.展开更多
虚假数据注入攻击(false data injection attack,FDIA)是智能电网安全与稳定运行面临的严重威胁。文中针对FDIA检测中存在的有标签数据稀少、正常和攻击样本极不平衡的问题,提出了融合无监督和有监督学习的FDIA检测算法。首先引入对比...虚假数据注入攻击(false data injection attack,FDIA)是智能电网安全与稳定运行面临的严重威胁。文中针对FDIA检测中存在的有标签数据稀少、正常和攻击样本极不平衡的问题,提出了融合无监督和有监督学习的FDIA检测算法。首先引入对比学习捕获少量攻击数据特征,生成新的攻击样本实现数据扩充;然后利用多种无监督检测算法对海量的无标签样本进行特征自学习,解决有标签样本稀缺的问题;最后将无监督算法提取的特征与历史特征集进行融合,在新的特征空间上构建有监督XGBoost分类器进行识别,输出正常或异常的检测结果。在IEEE 30节点系统上的算例分析表明,与其他FDIA检测算法相比,文中方法增强了FDIA检测模型在有标签样本稀少和数据不平衡情况下的稳定性,提升了FDIA的识别精度并降低了误报率。展开更多
为提高智能电网的安全性,结合传感器量测数据和攻击向量服从正态分布的特性,提出了一种基于高斯混合模型的虚假数据注入攻击(False Data Injection Attacks,FDIA)检测方法。在该方法中,通过EM算法求解出高斯混合模型参数,然后根据判断准...为提高智能电网的安全性,结合传感器量测数据和攻击向量服从正态分布的特性,提出了一种基于高斯混合模型的虚假数据注入攻击(False Data Injection Attacks,FDIA)检测方法。在该方法中,通过EM算法求解出高斯混合模型参数,然后根据判断准则,利用测试数据对高斯混合模型的分类效果进行验证。仿真实验结果表明,在IEEE-18和IEEE-30系统节点网络攻击检测中,基于高斯混合模型的FDIA检测相较于SVM的FDIA检测精度更好,但攻击强度和协方差矩阵是关键影响因素。展开更多
False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural ...False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.展开更多
【目的】网络的开放性导致综合能源系统易受网络攻击,研究综合能源系统(integrated energy system,IES)遭受虚假数据注入攻击(false data injection attack,FDIA)后的弹性提升问题。【方法】构造系统遭受到FDIA后的弹性提升框架,建立考...【目的】网络的开放性导致综合能源系统易受网络攻击,研究综合能源系统(integrated energy system,IES)遭受虚假数据注入攻击(false data injection attack,FDIA)后的弹性提升问题。【方法】构造系统遭受到FDIA后的弹性提升框架,建立考虑安全性及经济性的系统弹性评估方法。在分析能量流与信息流攻击机理的基础上,提出基于连续小波变换(continuous wavelet transform,CWT)和卷积神经网络(convolutional neural network,CNN)的能量流FDIA检测方法,以及基于CWT和广义回归神经网络模型(general regression neural network,GRNN)的信息流FDIA检测方法。进一步分析FDIA对系统调度造成的影响,建立优化调度模型,提升系统受到网络攻击后的弹性。【结果】含CWT+GRNN检测模型的综合能源系统弹性提升策略比不含检测模型的提升策略更具优越性,安全可靠性高出22.79%,运行经济性高出12.89%,弹性提升水平高出19.82%。含检测模型的综合能源系统弹性提升策略接近于不受网络攻击运行时的水平。【结论】基于CWT+GRNN检测模型的综合能源系统弹性提升策略在系统受FDIA后能够明显提升系统弹性,使系统性能接近于正常运行时的水平。展开更多
As a typical representative of the so-called cyber-physical system,smart grid reveals its high efficiency,robustness and reliability compared with conventional power grid.However,due to the deep integration of electri...As a typical representative of the so-called cyber-physical system,smart grid reveals its high efficiency,robustness and reliability compared with conventional power grid.However,due to the deep integration of electrical components and computinginformation in cyber space,smart grid is vulnerable to malicious attacks,especially for a type of attacks named false data injection attacks(FDIAs).FDIAs are capable of tampering meter measurements and affecting the results of state estimation stealthily,which severely threat the security of smart grid.Due to the significantinfluence of FDIAs on smart grid,the research related to FDIAs has received considerable attention over the past decade.This paper aims to summarize recent advances in FDIAs against smart grid state estimation,especially from the aspects of background materials,construction methods,detection and defense strategies.Moreover,future research directions are discussed and outlined by analyzing existing results.It is expected that through the review of FDIAs,the vulnerabilities of smart grid to malicious attacks can be further revealed and more attention can be devoted to the detection and defense of cyber-physical attacks against smart grid.展开更多
State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure...State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure-ment data and bypass the bad data detection(BDD)mechanism,leading to incorrect results of power system state estimation(PSSE).This paper presents a detection framework of FDIA for PSSE based on graph edge-conditioned convolutional networks(GECCN),which use topology information,node features and edge features.Through deep graph architecture,the correlation of sample data is effectively mined to establish the mapping relationship between the estimated values of measurements and the actual states of power systems.In addition,the edge-conditioned convolution operation allows processing data sets with different graph structures.Case studies are undertaken on the IEEE 14-bus system under different attack intensities and degrees to evaluate the performance of GECCN.Simulation results show that GECCN has better detection performance than convolutional neural networks,deep neural net-works and support vector machine.Moreover,the satisfactory detection performance obtained with the data sets of the IEEE 14-bus,30-bus and 118-bus systems verifies the effective scalability of GECCN.展开更多
【目的】随着新型电力系统中分布式节点广泛接入配电网,频繁的数据交互增加了配电网遭受虚假数据注入攻击(false data injection attacks,FDIA)的风险。常规的数据驱动检测方法在挖掘数据特征时往往将所有数据作为一个整体,忽略了不同...【目的】随着新型电力系统中分布式节点广泛接入配电网,频繁的数据交互增加了配电网遭受虚假数据注入攻击(false data injection attacks,FDIA)的风险。常规的数据驱动检测方法在挖掘数据特征时往往将所有数据作为一个整体,忽略了不同节点数据中的个性特征。针对这一问题,文章提出了一种基于最大信息系数的个性化联邦训练方法,用于分布式新能源场景下的虚假数据注入攻击检测。【方法】所提方法将检测模型部署在分布式边缘节点,提高了边缘节点的网络安全防护能力及本地数据隐私保护能力;通过应用多层神经网络进行个性化联邦训练,将其分为不同特征层来进行共性和个性特征分离,在分布式检测的基础上加强对异构节点数据的特征处理;考虑量测数据中的时间特征,通过引入最大信息系数深入挖掘数据中潜在的规律性特征,将分析结果融合个性化联邦训练,以提高对节点本身数据个性特征的提取能力。【结果】以含分布式新能源节点的园区数据为例进行仿真分析,所提方法相比传统联邦框架和不考虑相关性分析的检测方法,检测准确率、精确率、召回率和F1分数均有所提升;最大信息系数在处理周期性数据时具有较好的个性特征提取能力。【结论】所提方法增加了对数据共性和个性特征的分离和提取,在客户端数量较多时检测模型具有较快的收敛速率,更适合分布式新能源场景下的FDIA检测。展开更多
基金supported in part by the National Science Foundation of China(62373240,62273224,U24A20259).
文摘Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.
文摘配电变压器三相不平衡运行异常监测容易受到外界噪声干扰,导致监测结果出现偏差,因此提出结合虚假数据注入攻击(False Data Injection Attack,FDIA)检测模型和指数加权移动平均的异常监测方法。通过分布式FDIA检测模型,提取三相不平衡运行异常数据。利用指数加权移动平均方法,平滑处理误差。以滑动窗口的均值为中心,分析运行异常情况,计算三相电压不平衡度,实现三相不平衡运行异常监测。实验结果表明,该方法的监测准确性较高,为电力系统的安全稳定运行提供有力保障。
基金supported in part by the Sichuan Science and Technology Program(2024YFHZ0015)the Key Laboratory of Data Protection and Intelligent Management,Ministry of Education,Sichuan University(SCUSACXYD202401).
文摘False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.
文摘虚假数据注入攻击(false data injection attack,FDIA)是智能电网安全与稳定运行面临的严重威胁。文中针对FDIA检测中存在的有标签数据稀少、正常和攻击样本极不平衡的问题,提出了融合无监督和有监督学习的FDIA检测算法。首先引入对比学习捕获少量攻击数据特征,生成新的攻击样本实现数据扩充;然后利用多种无监督检测算法对海量的无标签样本进行特征自学习,解决有标签样本稀缺的问题;最后将无监督算法提取的特征与历史特征集进行融合,在新的特征空间上构建有监督XGBoost分类器进行识别,输出正常或异常的检测结果。在IEEE 30节点系统上的算例分析表明,与其他FDIA检测算法相比,文中方法增强了FDIA检测模型在有标签样本稀少和数据不平衡情况下的稳定性,提升了FDIA的识别精度并降低了误报率。
文摘为提高智能电网的安全性,结合传感器量测数据和攻击向量服从正态分布的特性,提出了一种基于高斯混合模型的虚假数据注入攻击(False Data Injection Attacks,FDIA)检测方法。在该方法中,通过EM算法求解出高斯混合模型参数,然后根据判断准则,利用测试数据对高斯混合模型的分类效果进行验证。仿真实验结果表明,在IEEE-18和IEEE-30系统节点网络攻击检测中,基于高斯混合模型的FDIA检测相较于SVM的FDIA检测精度更好,但攻击强度和协方差矩阵是关键影响因素。
基金supported in part by the the Natural Science Foundation of Shanghai(20ZR1421600)Research Fund of Guangxi Key Lab of Multi-Source Information Mining&Security(MIMS21-M-02).
文摘False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.
基金supported by the National Natural Science Foundation of China(Grant Nos.61822309,61773310&U1736205)
文摘As a typical representative of the so-called cyber-physical system,smart grid reveals its high efficiency,robustness and reliability compared with conventional power grid.However,due to the deep integration of electrical components and computinginformation in cyber space,smart grid is vulnerable to malicious attacks,especially for a type of attacks named false data injection attacks(FDIAs).FDIAs are capable of tampering meter measurements and affecting the results of state estimation stealthily,which severely threat the security of smart grid.Due to the significantinfluence of FDIAs on smart grid,the research related to FDIAs has received considerable attention over the past decade.This paper aims to summarize recent advances in FDIAs against smart grid state estimation,especially from the aspects of background materials,construction methods,detection and defense strategies.Moreover,future research directions are discussed and outlined by analyzing existing results.It is expected that through the review of FDIAs,the vulnerabilities of smart grid to malicious attacks can be further revealed and more attention can be devoted to the detection and defense of cyber-physical attacks against smart grid.
基金supported in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2020B010166004in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515111100+1 种基金in part by the National Natural Science Foundation of China under Grant 52207106in part by the Open Fund of State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems(China Electric Power Research Institute)under Grant KJ80-21-001.
文摘State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure-ment data and bypass the bad data detection(BDD)mechanism,leading to incorrect results of power system state estimation(PSSE).This paper presents a detection framework of FDIA for PSSE based on graph edge-conditioned convolutional networks(GECCN),which use topology information,node features and edge features.Through deep graph architecture,the correlation of sample data is effectively mined to establish the mapping relationship between the estimated values of measurements and the actual states of power systems.In addition,the edge-conditioned convolution operation allows processing data sets with different graph structures.Case studies are undertaken on the IEEE 14-bus system under different attack intensities and degrees to evaluate the performance of GECCN.Simulation results show that GECCN has better detection performance than convolutional neural networks,deep neural net-works and support vector machine.Moreover,the satisfactory detection performance obtained with the data sets of the IEEE 14-bus,30-bus and 118-bus systems verifies the effective scalability of GECCN.
文摘【目的】随着新型电力系统中分布式节点广泛接入配电网,频繁的数据交互增加了配电网遭受虚假数据注入攻击(false data injection attacks,FDIA)的风险。常规的数据驱动检测方法在挖掘数据特征时往往将所有数据作为一个整体,忽略了不同节点数据中的个性特征。针对这一问题,文章提出了一种基于最大信息系数的个性化联邦训练方法,用于分布式新能源场景下的虚假数据注入攻击检测。【方法】所提方法将检测模型部署在分布式边缘节点,提高了边缘节点的网络安全防护能力及本地数据隐私保护能力;通过应用多层神经网络进行个性化联邦训练,将其分为不同特征层来进行共性和个性特征分离,在分布式检测的基础上加强对异构节点数据的特征处理;考虑量测数据中的时间特征,通过引入最大信息系数深入挖掘数据中潜在的规律性特征,将分析结果融合个性化联邦训练,以提高对节点本身数据个性特征的提取能力。【结果】以含分布式新能源节点的园区数据为例进行仿真分析,所提方法相比传统联邦框架和不考虑相关性分析的检测方法,检测准确率、精确率、召回率和F1分数均有所提升;最大信息系数在处理周期性数据时具有较好的个性特征提取能力。【结论】所提方法增加了对数据共性和个性特征的分离和提取,在客户端数量较多时检测模型具有较快的收敛速率,更适合分布式新能源场景下的FDIA检测。
