• Laser & Optoelectronics Progress
  • Vol. 58, Issue 6, 610006 (2021)
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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    DOI: 10.3788/LOP202158.0610006 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Flowchart of CSA-BP algorithm
    Fig. 1. Flowchart of CSA-BP algorithm
    Training status of Ionosphere dataset
    Fig. 2. Training status of Ionosphere dataset
    Prediction accuracy of Iris dataset
    Fig. 3. Prediction accuracy of Iris dataset
    Prediction accuracy of Wine dataset
    Fig. 4. Prediction accuracy of Wine dataset
    Prediction accuracy of Glass dataset
    Fig. 5. Prediction accuracy of Glass dataset
    Prediction accuracy of Parkinson dataset
    Fig. 6. Prediction accuracy of Parkinson dataset
    Prediction accuracy of Ionosphere dataset
    Fig. 7. Prediction accuracy of Ionosphere dataset
    BP neural network convergence curve
    Fig. 8. BP neural network convergence curve
    GA-BP convergence curve
    Fig. 9. GA-BP convergence curve
    CS-BP convergence curve
    Fig. 10. CS-BP convergence curve
    CSA-BP convergence curve
    Fig. 11. CSA-BP convergence curve
    NO.DatasetFeatureSample sizeClassification
    D1Iris41503
    D2Wine131783
    D3Glass92146
    D4Parkinson221952
    D5Ionosphere343512
    Table 1. Dataset description
    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 3. Performance comparison of four algorithms in different datasets
    AlgorithmNumber of epochsAccuracy /%
    BP81589.2
    GA-BP57292.1
    CS-BP35894.3
    CSA-BP7996.6
    Table 4. Accuracy and number of epochs
    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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