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Laser & Optoelectronics Progress, Volume. 58, Issue 6, 610006(2021)

Intrusion Detection Method of BP Neural Network Based on Crow Search Algorithm

Lan Lüying1, Tang Xianghong1,2,3、*, Gu Xin1, and Lu Jianguang1,2,3
Author Affiliations
  • 1Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China
  • 2School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou 550025, China
  • 3Stata Key Laboratory of Public Big Data, Guizhou University, Guiyang, Guizhou 550025, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
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    References (16)
    Cited By (1)
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    Figures & Tables(14)
    Flowchart of CSA-BP algorithm

    Fig. 1. Flowchart of CSA-BP algorithm

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    Training status of Ionosphere dataset

    Fig. 2. Training status of Ionosphere dataset

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    Prediction accuracy of Iris dataset

    Fig. 3. Prediction accuracy of Iris dataset

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    Prediction accuracy of Wine dataset

    Fig. 4. Prediction accuracy of Wine dataset

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    Prediction accuracy of Glass dataset

    Fig. 5. Prediction accuracy of Glass dataset

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    Prediction accuracy of Parkinson dataset

    Fig. 6. Prediction accuracy of Parkinson dataset

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    Prediction accuracy of Ionosphere dataset

    Fig. 7. Prediction accuracy of Ionosphere dataset

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    BP neural network convergence curve

    Fig. 8. BP neural network convergence curve

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    GA-BP convergence curve

    Fig. 9. GA-BP convergence curve

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    CS-BP convergence curve

    Fig. 10. CS-BP convergence curve

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    CSA-BP convergence curve

    Fig. 11. CSA-BP convergence curve

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    • Table 1. Dataset description

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      Table 1. Dataset description

      NO.DatasetFeatureSample sizeClassification
      D1Iris41503
      D2Wine131783
      D3Glass92146
      D4Parkinson221952
      D5Ionosphere343512

    • Table 3. Performance comparison of four algorithms in different datasets

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      Table 3. Performance comparison of four algorithms in different datasets

      DatasetCSA-BPCS-BPGA-BPBP
      Trainingtime /sNumber of iterationsTrainingtime /sNumber of iterationsTrainingtime /sNumber of iterationsTrainingtime /sNumber of iterations
      D19.673412.644515.226215.7875
      D216.914228.286535.118038.5296
      D319.296331.147849.259552.16111
      D440.159660.5718972.1820785.24239
      D557.1517566.5222280.5525190.66288

    • Table 4. Accuracy and number of epochs

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      Table 4. Accuracy and number of epochs

      AlgorithmNumber of epochsAccuracy /%
      BP81589.2
      GA-BP57292.1
      CS-BP35894.3
      CSA-BP7996.6
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    Lan Lüying, Tang Xianghong, Gu Xin, Lu Jianguang. Intrusion Detection Method of BP Neural Network Based on Crow Search Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(6): 610006

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    Paper Information

    Category: Image Processing

    Received: Jul. 3, 2020

    Accepted: --

    Published Online: Mar. 1, 2021

    The Author Email: Xianghong Tang (lanlym249@163.com)

    DOI:10.3788/LOP202158.0610006

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology