• Study On Optical Communications
  • Vol. 50, Issue 3, 23012501 (2024)
Xianjun ZHOU, Ru WANG*, Hang LIU, and Bo JIN
Author Affiliations
  • School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
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    DOI: 10.13756/j.gtxyj.2024.230125 Cite this Article
    Xianjun ZHOU, Ru WANG, Hang LIU, Bo JIN. Research on False Data Injection Attack Identification based on CNN-CBAM[J]. Study On Optical Communications, 2024, 50(3): 23012501 Copy Citation Text show less
    Model and schematic diagram of FDIA
    Fig. 1. Model and schematic diagram of FDIA
    The structure of CNN model
    Fig. 2. The structure of CNN model
    The structure of CBAM
    Fig. 3. The structure of CBAM
    FDIA position detection method
    Fig. 4. FDIA position detection method
    The structure of IEEE14 node system
    Fig. 5. The structure of IEEE14 node system
    6 F1values of standard error of dynamic noise in different environments
    Fig. 6. 6 F1values of standard error of dynamic noise in different environments
    FDIA detection accuracy of standard error of dynamic noise in different environments
    Fig. 7. FDIA detection accuracy of standard error of dynamic noise in different environments
    系统模型IEEE14IEEE118
    线路的数量20186
    量测值的数量19180
    注入节点量测值的数量870
    功率流测量值的数量11170
    不能测量的线路数量27
    Table 1. Standards for power testing of IEEE14 and IEEE118 node systems
    网络结构网络层数精确率(%)召回率(%)F1(%)RACC(%)
    DNN391.2491.8091.5287.74
    492.9593.1993.0788.92
    592.5192.6992.6088.65
    691.4992.0891.7887.98
    CNN394.2495.6594.9495.60
    495.3496.7896.0596.70
    595.0196.4195.7096.30
    694.8795.9795.4296.00
    SSA-CNN396.2797.3396.7997.15
    496.5797.8297.1197.69
    597.1698.2397.6998.04
    697.0297.9397.4797.71
    CNN-CBAM397.3599.5299.4396.31
    497.9199.4899.5597.10
    598.8099.7699.7098.25
    698.1099.6399.6097.98
    Table 2. Performance comparison of different networks in IEEE14 node system
    网络结构网络层数精确率(%)召回率(%)F1(%)RACC(%)
    DNN391.9092.3492.1287.91
    492.4693.6793.0688.65
    592.5894.7993.6788.13
    693.0195.6294.3090.70
    CNN394.5493.3294.9088.73
    498.8898.4698.2992.66
    598.2898.6598.3392.35
    699.1498.4998.2490.79
    SSA-CNN396.8998.0797.4889.37
    497.6898.7998.2390.69
    598.4699.0098.7392.16
    699.5999.2899.4393.67
    CNN-CBAM397.8897.7797.8392.44
    499.4899.4499.4694.45
    5100.00100.00100.0095.94
    6100.00100.00100.0096.72
    Table 3. Performance comparison of different networks in IEEE118 node system
    Xianjun ZHOU, Ru WANG, Hang LIU, Bo JIN. Research on False Data Injection Attack Identification based on CNN-CBAM[J]. Study On Optical Communications, 2024, 50(3): 23012501
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