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Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart 被引量:1
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作者 Fengyong Li Weicheng Shen +1 位作者 Zhongqin Bi Xiangjing Su 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期2095-2115,共21页
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
关键词 Distributed smart grid FDIA adversarial learning power public-private network edge
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融合无监督和有监督学习的虚假数据注入攻击检测
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作者 黄冬梅 王一帆 +3 位作者 胡安铎 周游 时帅 胡伟 《电力工程技术》 北大核心 2024年第2期134-141,共8页
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
关键词 虚假数据注入攻击(FDIA) 有监督学习 无监督学习 对比学习 数据扩充 特征融合
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基于虚假数据注入攻击的网络安全检测 被引量:3
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作者 胡凯波 於立峰 +1 位作者 郑美芬 崔娜 《系统仿真技术》 2022年第1期58-63,共6页
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
关键词 FDIA检测 高斯混合模型 EM算法 仿真实验 CPS网络
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从模仿到创新中国IT业的必经之路
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作者 范靖 《计算机》 2003年第8期2-2,共1页
关键词 中国IT业 模仿 中国经济 FDIA 信息产业
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上海信息产业的民营之路在哪里?
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作者 李鹃 《计算机》 2003年第8期2-2,共1页
关键词 上海 信息产业 民营企业 FDIA 国际水平 劳动力成本
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Detection of false data injection attacks on power systems using graph edge-conditioned convolutional networks 被引量:1
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作者 Bairen Chen Q.H.Wu +1 位作者 Mengshi Li Kaishun Xiahou 《Protection and Control of Modern Power Systems》 SCIE EI 2023年第2期1-12,共12页
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
关键词 Power system state estimation(PSSE) Bad data detection(BDD) False data injection attacks(FDIA) Graph edge-conditioned convolutional networks(GECCN)
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False data injection attacks against smart grid state estimation:Construction, detection and defense 被引量:6
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作者 ZHANG Meng SHEN Chao +4 位作者 HE Ning HAN SiCong LI Qi WANG Qian GUAN XiaoHong 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2019年第12期2077-2087,共11页
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
关键词 false data injection attacks(fdias) state estimation smart grid cyber security
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智能电网中的虚假数据注入攻击检测方法研究 被引量:4
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作者 阮兆文 孟干 +1 位作者 周冬青 吴毅华 《自动化与仪器仪表》 2019年第3期49-52,共4页
针对当前FDIAs篡改电网数据,从而绕过传统的数据检测机制,最终导致控制中心根据这种虚假数据注入做出错误的判断,以此导致整个智能电网出现故障的问题,提出一种基于聚类算法与状态预测检测法的FDIAs检测技术。为实现FDIAs攻击检测,文章... 针对当前FDIAs篡改电网数据,从而绕过传统的数据检测机制,最终导致控制中心根据这种虚假数据注入做出错误的判断,以此导致整个智能电网出现故障的问题,提出一种基于聚类算法与状态预测检测法的FDIAs检测技术。为实现FDIAs攻击检测,文章首先对虚假数据注入攻击的原理进行分析,以此为后续的攻击检测提供理论基础;然后结合目前的攻击检测方法,提出节点电压稳定性的指标检测方法。通过构建NVSI与FDIAs之间的关系,结合NVSI值对FDIAs进行初步量化,以此对脆弱节点进行初步辨识,然后采用聚类算法对脆弱性的节点进行分类,并划分节点等级;最后通过状态预测检测法完成对虚假FDIAs的检测。通过仿真,并以IEEE14-bus标准测试系统作为基础,对上述的方法进行了验证,表明了本文方法的可行性。 展开更多
关键词 智能电网 NVSI值 状态预测检测法 fdias攻击检测
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基于多阶段博弈的电力CPS虚假数据注入攻击防御方法 被引量:19
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作者 蔡星浦 王琦 +1 位作者 邰伟 刘科研 《电力建设》 北大核心 2019年第5期48-54,共7页
信息通信技术的快速发展使电力系统成为典型的信息物理系统(cyber physical system, CPS)。在电网侧控制日趋智能化的同时,电力CPS也面临潜在的网络攻击风险。文章首先分析了针对电力CPS的虚假数据注入攻击(false data injection attack... 信息通信技术的快速发展使电力系统成为典型的信息物理系统(cyber physical system, CPS)。在电网侧控制日趋智能化的同时,电力CPS也面临潜在的网络攻击风险。文章首先分析了针对电力CPS的虚假数据注入攻击(false data injection attack,FDIA)的可行性,然后针对攻击方和防御方的多阶段动态交互过程,提出了一种基于博弈论的关键测量设备的分阶段动态防御方法,通过IEEE标准系统算例验证了所提方法的可行性和有效性。 展开更多
关键词 电力信息物理系统 网络攻击 零和博弈 虚假数据注入攻击(FDIA)
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