Table 1.
Comprehensive comparison of state-of-the-art approaches.
Fig 1.
Architecture of proposed system model.
Fig 2.
System model of the proposed method.
Fig 3.
Schematic representation of univariate ensemble-based feature selection.
Fig 4.
Flow diagram for the proposed methodology.
Table 2.
Simulation parameters of DDcGAN.
Table 3.
Simulation parameters of SDN network.
Table 4.
Performance metrics.
Fig 5.
Network simulation of the proposed method.
Fig 6.
Accuracy analysis of DDcGAN-GSOM with existing techniques.
Fig 7.
F1-score analysis of DDcGAN-GSOM with existing methods.
Fig 8.
Precision analysis of DDcGAN-GSOM with current approaches.
Fig 9.
Analysis of DDcGAN-GSOM delay with existing techniques.
Fig 10.
Energy consumption analysis of DDcGAN-GSOM with existing methods.
Fig 11.
Energy consumption analysis of DDcGAN-GSOM with existing approaches.
Fig 12.
Throughput analysis of DDcGAN-GSOM with prevailing approaches.
Fig 13.
True positive rate (TPR) analysis of DDcGAN-GSOM with existing techniques.
Fig 14.
False alarm rate analysis of DDcGAN-GSOM with existing approaches.
Fig 15.
Specificity comparison of DDcGAN-GSOM with prevailing methods.
Fig 16.
Discriminator loss of suggested technique.
Fig 17.
Generator loss of the proposed method.
Table 5.
Comparative analysis of DDcGAN-GSOM’s Accuracy, Precision, and F1-score.
Table 6.
Comparative analysis of DDcGAN-GSOM’s Energy Consumption, Throughput, and Dealy.
Table 7.
Comparative analysis of DDcGAN-GSOM’s True Positive Rate, False Alarm Rate, and Specificity.
Table 8.
Performance comparison of proposed system with state-of-the-art approaches.