2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于改进多隐层极限学习机的电网虚假数据注入攻击检测

席磊 何苗 周博奇 李彦营

席磊, 何苗, 周博奇, 李彦营. 基于改进多隐层极限学习机的电网虚假数据注入攻击检测. 自动化学报, 2023, 49(4): 881−890 doi: 10.16383/j.aas.c211127
引用本文: 席磊, 何苗, 周博奇, 李彦营. 基于改进多隐层极限学习机的电网虚假数据注入攻击检测. 自动化学报, 2023, 49(4): 881−890 doi: 10.16383/j.aas.c211127
Xi Lei, He Miao, Zhou Bo-Qi, Li Yan-Ying. Research on false data injection attack detection in power system based on improved multi layer extreme learning machine. Acta Automatica Sinica, 2023, 49(4): 881−890 doi: 10.16383/j.aas.c211127
Citation: Xi Lei, He Miao, Zhou Bo-Qi, Li Yan-Ying. Research on false data injection attack detection in power system based on improved multi layer extreme learning machine. Acta Automatica Sinica, 2023, 49(4): 881−890 doi: 10.16383/j.aas.c211127

基于改进多隐层极限学习机的电网虚假数据注入攻击检测

doi: 10.16383/j.aas.c211127
基金项目: 国家自然科学基金 (51707102), 信息物理融合防御与控制系统宜昌市重点实验室 (三峡大学) 开放基金 (2020XXRH04)资助
详细信息
    作者简介:

    席磊:三峡大学教授. 主要研究方向为电力系统运行与控制, 自动发电控制和智能控制方法. 本文通信作者. E-mail: xilei2014@163.com

    何苗:三峡大学电气工程专业硕士研究生. 主要研究方向为电力系统网络攻击. E-mail: he_miao98@163.com

    周博奇:三峡大学电气工程专业硕士研究生. 主要研究方向为电力系统运行与控制, 自动发电控制. E-mail: zhouforst@163.com

    李彦营:三峡大学电气工程专业硕士研究生. 主要研究方向为电力系统运行与控制, 自动发电控制. E-mail: li980604@163.com

Research on False Data Injection Attack Detection in Power System Based on Improved Multi Layer Extreme Learning Machine

Funds: Supported by National Natural Science Foundation of China (51707102) and Yichang Key Laboratory of Information Physics Fusion Defense and Control System (China Three Gorges University) (2020XXRH04)
More Information
    Author Bio:

    XI Lei Professor at China Three Gorges University. His research interest covers power system operation and control, automatic generation control, and intelligent control methods. Corresponding author of this paper

    HE Miao Master student in the Department of Electrical Engineering, China Three Gorges University. Her main research interest is power system network attack

    ZHOU Bo-Qi Master student in the Department of Electrical Engineering, China Three Gorges University. His research interest covers power system operation and control, and automatic generation control

    LI Yan-Ying Master student in the Department of Electrical Engineering, China Three Gorges University. Her research interest covers power system operation and control, and automatic generation control

  • 摘要: 虚假数据注入攻击(False data injection attacks, FDIA)严重威胁了电力信息物理系统(Cyber-physical system, CPS)的状态估计, 而目前大多数检测方法侧重于攻击存在性检测, 无法获取准确的受攻击位置. 故本文提出了一种基于灰狼优化(Gray wolf optimization, GWO)多隐层极限学习机(Multi layer extreme learning machine, ML-ELM)的电力信息物理系统虚假数据注入攻击检测方法. 所提方法将攻击检测看作是一个多标签二分类问题, 不仅将用于特征提取与分类训练的极限学习机由单隐层变为多隐层, 以解决极限学习机特征表达能力有限的问题, 且融入了具有强全局搜索能力的灰狼优化算法以提高多隐层极限学习机分类精度和泛化性能. 进而自动识别系统各个节点状态量的异常, 获取受攻击的精确位置. 通过在不同场景下对IEEE-14和57节点测试系统上进行大量实验, 验证了所提方法的有效性, 且分别与极限学习机、未融入灰狼优化的多隐层极限学习机以及支持向量机(Support vector machine, SVM)相比, 所提方法具有更精确的定位检测性能.
  • 图  1  面向电力CPS的FDIA结构图

    Fig.  1  FDIA structure diagram for power CPS

    图  2  ML-ELM网络结构

    Fig.  2  Network structure of ML-ELM

    图  3  GWO算法流程图

    Fig.  3  Flow chart for GWO algorithm

    图  4  IEEE-14节点系统污染状态检测准确率

    Fig.  4  Contaminated state detection accuracy for IEEE 14-bus system

    图  5  IEEE-14节点系统ROC曲线

    Fig.  5  ROC curves for IEEE 14-bus system

    图  6  IEEE-57节点系统污染状态检测准确率

    Fig.  6  Contaminated state detection accuracy for IEEE 57-bus system

    图  7  IEEE-57节点系统ROC曲线

    Fig.  7  ROC curves for IEEE 57-bus system

    表  1  IEEE-14节点系统性能评估指标结果

    Table  1  Performance evaluation index results for IEEE 14-bus system

    总线指标GWO-ML-ELMML-ELMELMSVM
    14节点精度0.98810.97740.94990.9133
    召回率0.97170.95710.90200.9023
    F10.97980.96710.92530.9078
    下载: 导出CSV

    表  2  IEEE-57节点系统性能评估指标结果

    Table  2  Performance evaluation index results for IEEE 57-bus system

    总线指标GWO-ML-ELMML-ELMELMSVM
    57节点精度0.95240.93140.88570.8940
    召回率0.91810.89460.80680.8273
    F10.93490.91260.84440.8594
    下载: 导出CSV
  • [1] 臧海祥, 郭镜玮, 黄蔓云, 卫志农, 孙国强, 俞文帅. 基于深度迁移学习的时变拓扑下电力系统状态估计. 电力系统自动化, 2021, 45(24): 49-56

    Zang Hai-Xiang, Guo Jing-Wei, Huang Man-Yun, Wei Zhi-Nong, Sun Guo-Qiang, Yu Wen-Shuai. State estimation for power systems with time-varying topology based on deep transfer learning. Automation of Electric Power Systems, 2021, 45(24): 49-56
    [2] 秦博雅, 刘东. 电网信息物理系统分析与控制的研究进展与展望. 中国电机工程学报, 2020, 40(18): 5816-5827

    Qin Bo-Ya, Liu Dong. Research progresses and prospects on analysis and control of cyber-physical system for power grid. Journal of Chinese Electrical Engineering Science, 2020, 40(18): 5816-5827
    [3] Ding D, Han Q L, Ge X, Wang J. Secure state estimation and control of cyber-physical systems: a survey. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(1): 176-190 doi: 10.1109/TSMC.2020.3041121
    [4] Liu Z, Wang L. Leveraging network topology optimization to strengthen power grid resilience against cyber-physical attacks. IEEE Transactions on Smart Grid, 2020, 12(2): 1552-1564
    [5] 刘莉, 翟登辉, 姜新丽. 电力系统不良数据检测与辨识方法的现状与发展. 电力系统保护与控制, 2010, 38(05): 143-147 doi: 10.3969/j.issn.1674-3415.2010.05.036

    Liu Li, Zhai Deng-Hui, Jiang Xin-Li. Current situation and development of the methods on bad-data detection and identification of power system. Power System Protection and Control, 2010, 38(05): 143-147 doi: 10.3969/j.issn.1674-3415.2010.05.036
    [6] Liu Y, Ning P, Reiter M K. False data injection attacks against state estimation in electric power grids. ACM Transactions on Information and System Security (TISSEC), 2011, 14(1): 1-33
    [7] Yan J, Yang G H, Wang Y. Dynamic reduced-order observer-based detection of false data injection attacks with application to smart grid systems. IEEE Transactions on Industrial Informatics, 2022 doi: 10.1109/TII.2022.3144445, to be published
    [8] Jorjani M, Seifi H, Varjani A Y. A graph theory-based approach to detect false data injection attacks in power system ac state estimation. IEEE Transactions on Industrial Informatics, 2021, 17(4): 2465-2475 doi: 10.1109/TII.2020.2999571
    [9] 朱杰, 张葛祥. 基于历史数据库的电力系统状态估计欺诈性数据防御. 电网技术, 2016, 40(6): 1772-1777

    Zhu Jie, Zhang Ge-Xiang. Defense against false data in power system state estimation based on historical database. Power System Technology, 2016, 40(6): 1772-1777
    [10] Zhao J, Zhang G, Scala M L, Zhao Y D, Chen C, Wang J. Short-term state forecasting-aided method for detection of smart grid general false data injection attacks. IEEE Transactions on Smart Grid, 2017, 8(4): 1580-1590 doi: 10.1109/TSG.2015.2492827
    [11] 罗小元, 潘雪扬, 王新宇, 关新平. 基于自适应Kalman滤波的智能电网假数据注入攻击检测. 自动化学报, 2020, 41(x): 1-12 doi: 10.16383/j.aas.c190636

    Luo Xiao-Yuan, Pan Xue-Yang, Wang Xin-Yu, Guan Xin-Ping. Detection of false data injection attack in smart grid via adaptive kalman filtering. Acta Automatica Sinica, 2020, 41(x): 1-12 doi: 10.16383/j.aas.c190636, to be published
    [12] 刘鑫蕊, 常鹏, 孙秋野. 基于XGBoost和无迹卡尔曼滤波自适应混合预测的电网虚假数据注入攻击检测. 中国电机工程学报, 2021, 41(16): 5462-5476

    Liu Xin-Rui, Chang Peng, Sun Qiu-Ye. Grid false data injection attacks detection based on xgboost and unscented kalman filter adaptive hybrid prediction. Journal of Chinese Electrical Engineering Science, 2021, 41(16): 5462-5476
    [13] Zhang Y, Wang J, Chen B. Detecting false data injection attacks in smart grids: a semi-supervised deep learning approach. IEEE Transactions on Smart Grid, 2021, 12(1): 623-634 doi: 10.1109/TSG.2020.3010510
    [14] 李元诚, 曾婧. 基于改进卷积神经网络的电网假数据注入攻击检测方法. 电力系统自动化, 2019, 43(20): 97-104 doi: 10.7500/AEPS20180919001

    Li Yuan-Cheng, Zeng Jing. Detection method of false data injection attack on power grid based on improved convolutional neural network. Automation of Electric Power Systems, 2019, 43(20): 97-104 doi: 10.7500/AEPS20180919001
    [15] Ashrafuzzaman M, Das S, Chakhchoukh Y, Shiva S, Sheldon F T. Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning.Computers and Security, 2020, 97: Article No. 101994
    [16] Wu T, Xue W, Wang H, Chung C Y, Wang G, Peng J, et al. Extreme learning machine-based state reconstruction for automatic attack filtering in cyber physical power system. IEEE Transactions on Industrial Informatics, 2021, 17(3): 1892-1904 doi: 10.1109/TII.2020.2984315
    [17] Wang S, Bi S, Zhang Y J A. Locational detection of the false data injection attack in a smart grid: A multilabel classification approach. IEEE Internet of Things Journal, 2020, 7(9): 8218-8227 doi: 10.1109/JIOT.2020.2983911
    [18] 徐睿, 梁循, 齐金山, 李志宇, 张树森. 极限学习机前沿进展与趋势. 计算机学报, 2019, 42(07): 1640-1670

    Xu Rui, Liang Xun, Qi Jin-Shan, Li Zhi-Yu, Zhang Shu-Sen. Advances and trends in extreme learning machine. Chinese Journal of Computers, 2019, 42(07): 1640-1670
    [19] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Advances in engineering software, 2014, 69: 46-61 doi: 10.1016/j.advengsoft.2013.12.007
    [20] Deng R, Xiao G, Lu R, Liang H, Vasilakos A V. False data injection on state estimation in power systems-Attacks, impacts, and defense: a survey. IEEE Transactions on Industrial Informatics, 2016, 13(2): 411-423
    [21] James J Q, Hou Y, Li V O K. Online false data injection attack detection with wavelet transform and deep neural networks. IEEE Transactions on Industrial Informatics, 2018, 14(7): 3271-3280 doi: 10.1109/TII.2018.2825243
    [22] Wu T, Chung C Y, Kamwa I. A fast state estimator for systems including limited number of pmus. IEEE Transactions on Power Systems, 2017, 32(6): 4329-4339 doi: 10.1109/TPWRS.2017.2673857
    [23] 冯晓萌, 孙秋野, 王冰玉, 高嘉文. 基于蠕虫传播和FDI的电力信息物理协同攻击策略. 自动化学报, 2020, 45(x): 1-13 doi: 10.16383/j.aas.c190574

    Feng Xiao-Meng, Sun Qiu-Ye, Wang Bing-Yu, Gao Jia-Wen. The coordinated cyber physical power attack strategy based on worm propagation and false data injection. Acta Automatica Sinica, 2020, 45(x): 1-13 doi: 10.16383/j.aas.c190574, to be published
    [24] Tu C, He X, Liu X, Li P. Cyber-attacks in PMU-based power network and countermeasures. IEEE Access, 2018, 6: 65594-65603 doi: 10.1109/ACCESS.2018.2878436
    [25] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications. Neurocomputing, 2006, 70(1/3): 489-501
    [26] Xue D, Jing X, Liu H. Detection of false data injection attacks in smart grid utilizing elm-based ocon framework. IEEE Access, 2019, 7: 31762-31773 doi: 10.1109/ACCESS.2019.2902910
    [27] Huang G B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems Man, and Cybernetics, Part B(Cybernetics), 2012, 42(2): 513-529 doi: 10.1109/TSMCB.2011.2168604
    [28] Kasun L, Zhou H, Huang G B, Vong C M. Representational learning with elms for big data. IEEE Intelligent Systems, 2013, 28(6): 31-34
    [29] Hou G, Xuan M, Zhang Y. A new method for intrusion detection using manifold learning algorithm. Telkomnika Indonesian Journal of Electrical Engineering, 2013, 11(12): 7339-7343
    [30] Yadav S, Shukla S. Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In: Proceedings of the IEEE 6th International Conference on Advanced Computing (IACC). Bhimavaram, India: IEEE, 2016. 78−83
    [31] 王琦, 邰伟, 汤奕, 倪明. 面向电力信息物理系统的虚假数据注入攻击研究综述. 自动化学报, 2019, 45(1): 72-83

    Wang Qi, Tai Wei, Tang Yi, Ni Ming. A review on false data injection attack toward cyber-physical power system. Acta Automatica Sinica, 2019, 45(1): 72-83
  • 加载中
图(7) / 表(2)
计量
  • 文章访问数:  928
  • HTML全文浏览量:  544
  • PDF下载量:  164
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-11-29
  • 录用日期:  2022-03-14
  • 网络出版日期:  2022-04-24
  • 刊出日期:  2023-04-20

目录

    /

    返回文章
    返回