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                    基于拉普拉斯特征映射學習的隱基于拉普拉斯特征映射學習的隱

                    石家宇 陳博 俞立

                    石家宇, 陳博, 俞立. 基于拉普拉斯特征映射學習的隱基于拉普拉斯特征映射學習的隱. 自動化學報, 2020, 45(1): 1?7 doi: 10.16383/j.aas.c190551
                    引用本文: 石家宇, 陳博, 俞立. 基于拉普拉斯特征映射學習的隱基于拉普拉斯特征映射學習的隱. 自動化學報, 2020, 45(1): 1?7 doi: 10.16383/j.aas.c190551
                    SHI Jia-Yu, Chen Bo, Yu Li. Stealthy FDI attack detection based on laplacian eigenmaps learning strategy. Acta Automatica Sinica, 2020, 45(1): 1?7 doi: 10.16383/j.aas.c190551
                    Citation: SHI Jia-Yu, Chen Bo, Yu Li. Stealthy FDI attack detection based on laplacian eigenmaps learning strategy. Acta Automatica Sinica, 2020, 45(1): 1?7 doi: 10.16383/j.aas.c190551

                    基于拉普拉斯特征映射學習的隱基于拉普拉斯特征映射學習的隱

                    doi: 10.16383/j.aas.c190551
                    基金項目: 國家自然科學基金項目(61973277, 61673351), 浙江省自然科學基金項目(LR20F030004)資助
                    詳細信息
                      作者簡介:

                      石家宇:浙江工業大學碩士研究生. 主要研究方向為信息物理系統安全. E-mail: jiayu_shi0621@163.com

                      陳博:浙江工業大學信息工程學院教授. 主要研究方向為信息融合, 攻擊信號檢測, 安全估計與控制, 信息物理系統. 本文通信作者. E-mail: bchen@aliyun.com

                      俞立:浙江工業大學信息工程學院教授. 主要研究方向為網絡化控制, 信息融合, 信息物理系統. E-mail: lyu@zjut.edu.cn

                    • 中圖分類號: Y

                    Stealthy FDI Attack Detection Based on Laplacian Eigenmaps Learning Strategy

                    Funds: Supported by the National Natural Science Foundation of China (61973277, 61673351), and Zhejiang Provincial Natural Science Foundation of China (LR20F030004)
                    • 摘要: 智能電網中的隱匿虛假數據入侵(False Data Injection,FDI)攻擊能夠繞過壞數據檢測機制, 導致控制中心做出錯誤的狀態估計, 進而干擾電力系統的正常運行. 由于電網系統具有復雜的拓撲結構, 故基于傳統機器學習的攻擊信號檢測方法存在維度過高帶來的過擬合問題, 而深度學習檢測方法則存在訓練時間長、占用大量計算資源的問題. 為此, 針對智能電網中的隱匿FDI攻擊信號, 提出了基于拉普拉斯特征映射降維的神經網絡檢測學習算法, 不僅降低了陷入過擬合的風險, 同時也提高了隱匿FDI攻擊檢測學習算法的泛化能力. 最后, 在IEEE57-Bus電力系統模型中驗證了所提方法的優點和有效性.
                    • 圖  1  基于拉普拉斯特征映射降維學習的檢測機制

                      Fig.  1  Detection mechanism based on laplacian eigenmaps

                      圖  2  神經網絡示意圖

                      Fig.  2  Neural network

                      圖  3  IEEE 57-Bus系統

                      Fig.  3  IEEE 57-Bus system

                      圖  4  隱匿FDI攻擊對系統狀態估計的影響

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

                      圖  5  節點30的狀態變化曲線

                      Fig.  5  The state curve of node 30

                      圖  6  不同環境噪聲下的殘差變化

                      Fig.  6  Residual change under different environmental noise

                      圖  7  LE降維后的樣本點分布

                      Fig.  7  Sample distribution after LE dimension reduction

                      圖  8  PCA降維后的樣本點分布

                      Fig.  8  Sample distribution after PCA dimension reduction

                      圖  9  收斂效果

                      Fig.  9  Convergence Performance

                      圖  10  四種檢測機制在不同隱患測量數k下的檢測精度ACC

                      Fig.  10  Detection accuracy of four detection mechanisms

                      圖  11  四種檢測機制在不同隱患測量數k下的誤報率FPR

                      Fig.  11  The false positive rate of four detection mechanisms

                      圖  12  四種檢測方法在不同環境噪聲中的檢測精度ACC變化

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

                      圖  13  四種檢測方法在不同環境噪聲中的誤報率FPR變化

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

                      圖  14  閾值$\tau$對檢測精度的影響

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

                      360彩票
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                    • HTML全文瀏覽量:  528
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                    出版歷程
                    • 收稿日期:  2019-07-26
                    • 錄用日期:  2019-12-15
                    • 網絡出版日期:  2020-01-06

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