## 4 Results and Discussion
### Comparative Analysis  
The proposed system has been compared with conventional methods in relation to the three considered datasets, and the obtained outcomes are presented in this section. The existing models, namely, Global Model, Local Model, Local Outlier Factor, Isolation Forest, GAN-AE (Generative Adversarial Network-Autoencoder), One Class SVM (One Class Support Vector Machine) and K-means, have been compared with the proposed system by considering the UNSW-Bot dataset, and the attained outcomes are presented in Table 10.  
From Table 10, we can see that existing algorithms such as K-means achieved 0.9711 accuracy rate and GAN-AE achieved a 0.9512 accuracy rate, while the Local Model showed a 0.9297 accuracy rate, whereas the proposed model achieved a high accuracy rate of 0.9978. Similarly, the precision, F1-Score and recall rate of the proposed work are better than those of existing methods. Following this, conventional models, namely, C5.0, RF (Random Forest), SVM (Support Vector Machine) and NB (Naive Bayes), have been compared with the proposed work by considering the CICIDS2017 dataset, and the outcomes are shown in Table 11. \begin{table}
\begin{tabular}{l l l l l} \hline
**Model** & **Precision** & **Accuracy** & **F1-Score** & **Recall** \\ \hline Global Model & 0.9933 & 0.9711 & 0.9831 & 0.9733 \\ \hline K-means & 0.9564 & 0.8801 & 0.9198 & 0.8676 \\ \hline One Class SVM & 0.9427 & 0.5301 & 0.667 & 0.5274 \\ \hline Local Model & 0.9654 & 0.9297 & 0.9426 & 0.921 \\ \hline GAN-AE & 0.9874 & 0.9512 & 0.9603 & 0.9364 \\ \hline Isolation Forest & 0.9008 & 0.7998 & 0.8397 & 0.8726 \\ \hline Local Outlier Factor & 0.8093 & 0.6063 & 0.6502 & 0.4911 \\ \hline Proposed & 0.9987 & 0.9978 & 0.9971 & 0.9975 \\ \hline \end{tabular}
\end{table} 
Table 10: Comparative analysis with UNSW-Bot [33]. Figure 6: Confusion matrix for (**a**) binary classes and (**b**) different classes in CICIDS2017. Table 11 shows that existing methodologies such as SVM reached a 0.79887 accuracy rate, while NB reached a 0.79996 accuracy rate, RF reached a 0.86803 accuracy rate and C5.0 reached a 0.86457 accuracy rate. However, the proposed model achieved a 0.98 accuracy rate. Moreover, the proposed model showed a better performance as regards precision and recall. Finally, a comparison has been undertaken with existing methodologies in relation to the NSL-KDD dataset. The conventional methods that have been considered include CFS + ANN (Correlation Feature Selection + Artificial Neural Network), SVM-RBF (Support Vector Machine-Radial Basis Function), SAE-SVM-RBF (Stacked Auto Encoder-SVM-RBF), CNN-Bi-LSTM (Convolutional Neural Network-Bi-directional Long Short-Term Memory), ensemble model, CART (Classification and Regression Tree), RF, C4.5, NB and MLP (Multi-Layer Perceptron). The respective results are shown in Table 12.  
Table 12 shows that the existing methodologies, such as CFS + ANN, achieved 99.31 specificity, while CNN-Bi-LSTM reached 80.83% specificity, and other algorithms such as SAE-SVM reached 98.35 and SVM-RBF reached 91.84. However, the proposed model reached a high specificity rate of 99.97. Similarly, conventional models such as CNN reach a 95.01 accuracy rate, SAE-SVM-RB showed a 95.27 accuracy rate, SVM-RBF showed 92.55 and CFS + ANN showed 97.49. The proposed system reached a 99.92 accuracy rate. Hence, the comparative analysis shows that the proposed method reached an accuracy rate of 99.92 with the NSL-KDD dataset, a 0.98 accuracy rate with the CICIDS2017 dataset and a 99.783 accuracy rate with the UNSW-Bot dataset. Further, by assigning different weights to accuracy scores, the overall weighted accuracy was influenced. If there are instances with higher weights, the model prioritizes them