## 5 Experimentation and Result Analysis
### Comparative Analysis of Proposed Model  
To highlight the efficacy of the proposed model, the outcome of the model associated with the other existing approaches that include LSSVM-IDS, AE-CGAN-RF, RNN-IDS, and SSAE-LSTM is presented. The result of the proposed model deliberates higher accuracy than other compared models such as LSSVM-IDS, AE-CGAN-RF, RNN-IDS, and SSAE-LSTM. Figure 7 provides the accuracy of the proposed approach and other existing approaches on NSL-KDD+ and NSL-KDD+20 datasets. The LSSVM-IDS model offers an accuracy of 53.21% and 55.86% on NSL-KDD+ and NSL-KDD+20 datasets. The performance of LSSVM-IDS shows poor performance compared to the AE-CGAN-RF approach. In addition, AE-CGAN-RF offers 64.56% and 67.12% accuracy on NSL-KDD+ and NSL-KDD+20 datasets. However, the outcome of AE-CGAN-RF provides an unsatisfactory performance. Consequently, the RNN-IDS and SSAE-LSTM approaches provide satisfactory accuracy outcomes of 81.42% and 84.93% and 85.98% and 88.79% on the NSL-KDD+ and NSL-KDD+20 datasets. Simultaneously, the formulated approach offers higher accuracies of 91.76% and 95.16% on NSL-KDD+ and NSL-KDD+20 datasets. Figure 5: Specificity rate of different classes on NSL-KDD+ and NSL-KDD+20 datasets. Figure 6: F1-score of different classes on NSL-KDD+ and NSL-KDD+20 datasets. To highlight the efficacy of the proposed model, the outcome of the model associated with the other existing approaches that include LSSVM-IDS, AE-CGAN-RF, RNN-IDS, and SSAE-LSTM is presented. The result of the proposed model deliberates higher accuracy than other compared models such as LSSVM-IDS, AE-CGAN-RF, RNN-IDS, and SSAE-LSTM. Figure 7 provides the accuracy of the proposed approach and other existing approaches on NSL-KDD+ and NSL-KDD+20 datasets. The LSSVM-IDS model offers an accuracy of 53.21% and 55.86% on NSL-KDD+ and NSL-KDD+20 datasets. The performance of LSSSVM-IDS shows poor performance compared to the AE-CGAN-RF approach. In addition, AE-CGAN-RF offers 64.56% and 67.12% accuracy on NSL-KDD+ and NSL-KDD+20 datasets. However, the outcome of AE-CGAN-RF provides an unsatisfactory performance. Consequently, the RNN-IDS and SSAE-LSTM approaches provide satisfactory accuracy outcomes of 81.42% and 84.93% and 85.98% and 88.79% on the NSL-KDD+ and NSL-KDD+20 datasets. Simultaneously, the formulated approach offers higher accuracies of 91.76% and 95.16% on NSL-KDD+ and NSL-KDD+20 datasets. Table 2 compares the overall performance of the proposed model to that of other comparable models on the NSL-KDD+ and NSL-KDD+20 datasets. To ensure the effectiveness of the proposed model, the accuracy, recall, FAR, and F1-score of LSSVM-IDS, AE-CGAN-RF, RNN-IDS, and SSAE-LSTM were measured. When compared to previous models, the suggested proposed model has a higher accuracy and recall rate. Furthermore, it is obvious that the suggested model achieves 1.85% and 1.06% false alarm rates for the NSL-KDD+ and NSL-KDD+20 datasets, respectively, while the comparison model fails to reach higher FPR outcomes. On the other hand, the suggested model's F1-score outperforms the NSL-KDD+ and NSL-KDD+20 datasets. Based on the comparative result analysis, we conclude that the proposed model identifies various classes of known and unknown assaults by boosting the learning accuracy of low-dimensional characteristics. The proposed model's overall performance expresses