## 4 Results and Discussion
### Performance Analysis
#### 4.3.1 Analysis with UNSW-Bot Dataset  
Initially, the proposed system was assessed with regard to four standard metrics for theUNSW-Bot dataset, and the corresponding outcomes are given in Table 1, with the relevant pictorial depiction shown in Figure 4.  
From Table 1, we see that the proposed system achieved a 99.783 Accuracy rate, a 99.705 F1-Score, a 99.875 Precision rate and a 99.748 Recall rate. Following this, the accuracy values for varied weights in the UNSW-Bot dataset have been assessed, and the analytical outcomes derived are given in Table 2.  
Table 2 shows that the accuracy value differs based on the weight. Accordingly, when weight = 1, the accuracy value is 68.476. This rate eventually increased with the increase in weights, and it reached 99.695 when weight = 9 and 99.783 when weight = 10. Subsequently, the outcomes of k-fold cross-validation for the UNSW-Bot dataset are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Weight** & **Accuracy** \\ \hline
1 & 68.476 \\ \hline
2 & 68.476 \\ \hline
3 & 99.703 \\ \hline
4 & 99.706 \\ \hline
5 & 99.686 \\ \hline
6 & 68.476 \\ \hline
7 & 99.723 \\ \hline
8 & 68.476 \\ \hline
9 & 99.695 \\ \hline
10 & 99.783 \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy for varied weights in the UNSW-Bot dataset.  
Figure 4: Confusion matrix for (**a**) binary classes and (**b**) different classes in UNSW-Bot.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
99.783 & 99.748 & 99.875 & 99.705 \\ \hline \end{tabular}
\end{table}
Table 1: Analysis with the UNSW-Bot dataset.  
Table 3 shows that the accuracy value differs for each fold. Accordingly, the accuracy value was found to be 0.9971 for fold 1, 0.9973 for fold 2, 0.9964 for fold 3, 0.997 for fold 4 and 0.9969 for fold 5. Hence, the accuracy value differs based on the folds. Further, a confusion matrix for the UNSW-Bot dataset has been constructed so as to expose the correct and misclassification rates of the proposed system. The corresponding outcomes are shown in Figure 4.  
A confusion matrix is calculated by comparing the predicted classifications of a model with the actual classifications in a dataset. It is often used in the context of classification problems. The matrix has four components: TP, denoting instances correctly predicted as positive; FP, as instances incorrectly predicted as positive; TN, signifying instances correctly predicted as negative; FN, as instances incorrectly predicted as negative. \"Attack classes\" refer to distinct categories of cyber threats that likely fit into specific malicious patterns. Evaluating the model's performance involves analyzing numerical values and statistics, including the distribution of attack classes in the dataset, developing a confusion matrix detailing true positives and false positives, and outlining class-specific metrics like precision and recall. These values offer insights into the model's ability to accurately detect various types of cyber-attacks.  
In Figure 4, the number of correct classifications of attack classes is shown to be 10,976, while, the correct classification of normal classes numbered 23,984. Concurrently, 30 normal classes have been misinterpreted as attack classes. On the contrary, 79 attack classes have been misinterpreted as normal. As the correct classification rate seems to be higher than the misclassification rate, the proposed system has been confirmed to be effective.