基于拉普拉斯特征映射学习的隐匿FDI攻击检测

石家宇; 陈博; 俞立

doi:10.16383/j.aas.c190551

基于拉普拉斯特征映射学习的隐匿FDI攻击检测

doi: 10.16383/j.aas.c190551

石家宇^{1, 2,},
陈博^{1, 2,},
俞立^{1, 2,}

1.
浙江工业大学信息工程学院杭州 310023
2.
浙江工业大学网络空间安全研究院杭州 310023

基金项目: 国家自然科学基金项目(61973277, 61673351), 浙江省自然科学基金项目(LR20F030004)资助

详细信息

作者简介:
石家宇：浙江工业大学硕士研究生. 主要研究方向为信息物理系统安全. E-mail: jiayu_shi0621@163.com

陈博：浙江工业大学信息工程学院教授. 主要研究方向为信息融合, 攻击信号检测, 安全估计与控制, 信息物理系统. 本文通信作者. E-mail: bchen@aliyun.com

俞立：浙江工业大学信息工程学院教授. 主要研究方向为网络化控制, 信息融合, 信息物理系统. E-mail: lyu@zjut.edu.cn

中图分类号: Y
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出版历程
- 收稿日期: 2019-07-26
- 录用日期: 2019-12-15
- 网络出版日期: 2020-01-06
- 刊出日期: 2021-10-20

Stealthy FDI Attack Detection Based on Laplacian Eigenmaps Learning Strategy

SHI Jia-Yu^{1, 2
,},
CHEN Bo^{1, 2
,},
YU Li^{1, 2
,}

1.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023
2.
Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023

Funds: Supported by National Natural Science Foundation of China (61973277, 61673351) and Zhejiang Provincial Natural Science Foundation of China (LR20F030004)

More Information

Author Bio:
SHI Jia-Yu　Master student at Zhejiang University of Technology. His main research interest is cyber-physical systems security

CHEN Bo　Professor at the College of Information Engineering, Zhejiang University of Technology. His research interest covers information fusion, attack signal detection, security estimation and control, and cyber physical system. Corresponding author of this paper

YU Li　Professor at the College of Information Engineering, Zhejiang University of Technology. His research interest covers networked control, information fusion, and cyber physical system

摘要

摘要: 智能电网中的隐匿虚假数据入侵(False data injection, FDI)攻击能够绕过坏数据检测机制, 导致控制中心做出错误的状态估计, 进而干扰电力系统的正常运行. 由于电网系统具有复杂的拓扑结构, 故基于传统机器学习的攻击信号检测方法存在维度过高带来的过拟合问题, 而深度学习检测方法则存在训练时间长、占用大量计算资源的问题. 为此, 针对智能电网中的隐匿FDI攻击信号, 提出了基于拉普拉斯特征映射降维的神经网络检测学习算法, 不仅降低了陷入过拟合的风险, 同时也提高了隐匿FDI攻击检测学习算法的泛化能力. 最后, 在IEEE57-Bus电力系统模型中验证了所提方法的优点和有效性.
- 智能电网 /
- 隐匿虚假数据入侵攻击 /
- 拉普拉斯特征映射 /
- 神经网络
Abstract: The stealthy false data injection (FDI) attack in smart grids can bypass the bad data detection, making an incorrect state estimate in the control center, which in turn interferes with the normal operation of the power system. Considering the complex topology of the grid system, the machine learningbased methods has an over-fitting problem caused by high dimensionality, while deep learning-based methods are subject to long training time and occupy a lot of computing resources. Motivated by the above fact, a neural network learning algorithm based on dimensional reduction of Laplacian eigenmaps (LE) is developed in this paper to detect hidden FDI attack signal in the smart grids. The proposed method not only reduces the risk of over-fitting, but also improves the generalization ability of the stealthy FDI attack detection learning algorithm. Finally, IEEE 57-Bus power system is employed to show the advantages and effectiveness of the proposed method.
- Smart grids /
- stealthy false data injection attack /
- Laplacian eigenmaps (LE) /
- neural network

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图 1 基于拉普拉斯特征映射降维学习的检测机制

Fig. 1 Detection mechanism based on Laplacian eigenmaps

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图 2 神经网络示意图

Fig. 2 Neural network

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图 3 IEEE 57-Bus系统

Fig. 3 IEEE 57-Bus system

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图 4 隐匿FDI攻击对系统状态估计的影响

Fig. 4 The effect of stealthy FDI attack on system state estimation

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图 5 节点30的状态变化曲线

Fig. 5 The state curve of node 30

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图 6 不同环境噪声下的残差变化

Fig. 6 Residual change under different environmental noise

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图 7 LE降维后的样本点分布

Fig. 7 Sample distribution after LE dimension reduction

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图 8 PCA降维后的样本点分布

Fig. 8 Sample distribution after PCA dimension reduction

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图 9 收敛效果

Fig. 9 Convergence performance

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图 10 四种检测机制在不同隐患测量数k下的检测精度ACC

Fig. 10 Detection accuracy of four detection mechanisms

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图 11 四种检测机制在不同隐患测量数k下的误报率FPR

Fig. 11 The false positive rate of four detection mechanisms

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图 12 四种检测方法在不同环境噪声中的检测精度ACC变化

Fig. 12 Detection accuracy of three detection mechanisms in different environmental noises

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图 13 四种检测方法在不同环境噪声中的误报率FPR变化

Fig. 13 False positive rate of three detection mechanisms in different environmental noises

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图 14 阈值$\tau$对检测精度的影响

Fig. 14 The effect of threshold $\tau$ on detection accuracy

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