## 5 Discussion  
Regarding the binary classification of network data, the best 20 features showed an increase in classification accuracy. Only the dataset generated was utilized in the process of binary classification, where we can see that the ML models are able to identify the normal behaving network and attack network with optimal accuracy. The MLP model here takes the highest training time, but predicts quickly. In KNN, an optimal number of neighbors, as discussed in the earlier section, also enabled the model to perform accurately. Data-preprocessing techniques and Z-score normalization also play a significant role. With the imbalanced dataset, imblearn also aided the model with the best data-preprocessing analysis. On the contrary, with 99.9872%, the random forest outshines the other models and has a better average in time management as well. The classification result analysis for the generated dataset is illustrated in Figure 10. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Class** & **Precision** & **Recall** & **F1 Score** & **Accuracy** & \
\begin{tabular}{l} **Training** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{l} **Prediction** \\ **Time (s)** \\ \end{tabular} \\ \hline \multirow{8}{*}{Random Forest} & 0 & 1.00 & 1.00 & 1.00 & & & \\  & 1 & 1.00 & 1.00 & 1.00 & & \\  & 2 & 1.00 & 1.00 & 1.00 & & \\  & 3 & 1.00 & 1.00 & 1.00 & & \\  & 4 & 1.00 & 1.00 & 1.00 & & \\  & 5 & 1.00 & 1.00 & 1.00 & & \\  & 6 & 1.00 & 1.00 & 1.00 & & \\  & 7 & 1.00 & 1.00 & 1.00 & & \\  & 8 & 1.00 & 1.00 & 1.00 & & \\ \hline \multirow{8}{*}{MLP} & 0 & 1.00 & 1.00 & 1.00 & & & \\  & 1 & 1.00 & 0.99 & 1.00 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & \\  & 3 & 1.00 & 1.00 & 1.00 & & \\  & 4 & 1.00 & 1.00 & 1.00 & 0.994109 & 2967.15 & 1454.12 \\  & 5 & 1.00 & 1.00 & 1.00 & & & \\  & 6 & 1.00 & 1.00 & 1.00 & & & \\  & 7 & 0.95 & 0.99 & 0.97 & & & \\  & 8 & 0.99 & 0.97 & 0.98 & & & \\ \hline \multirow{8}{*}{CNN} & 0 & 0.99 & 0.99 & 0.99 & & & \\  & 1 & 0.99 & 0.99 & 0.99 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 1.00 & 1.00 & 1.00 & & & \\  & 4 & 0.99 & 1.00 & 0.99 & 0.996718 & 6504.15 & 4254.12 \\  & 5 & 0.98 & 0.99 & 0.98 & & & \\  & 6 & 0.98 & 0.96 & 0.97 & & & \\  & 7 & 0.99 & 0.99 & 0.99 & & & \\  & 8 & 0.99 & 0.99 & 0.99 & & & \\ \hline \multirow{8}{*}{ Ensemble Approach} & 0 & 1.00 & 1.00 & 1.00 & & & \\  & 1 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 2 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 3 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 4 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 5 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 6 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 7 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 8 & 1.00 & 1.00 & 1.00 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._One-vs-rest (OvR) schemes were utilized to train classifiers in multiclass classification. This involves training multiple binary classifiers to help differentiate a single class. Categorical cross-entropy loss function was utilized for the multiclass neural network classification with soft max activation for the output layer and adam optimizer for the gradient-based optimization algorithm to train multiclass convolutional neural networks. The ensemble approach was utilized as a hybrid of both the hard and soft voting techniques. Hard voting provides the majority voting for the classifier with the highest outcome, and soft voting utilizes the idea of taking the average of predicted probabilities for each class from all the ensembled classifiers. The ensemble algorithm trains different models on training data, then computes the average prediction confidence for each test instance and sets a threshold to determine which should be enacted for the outcome. Figure 11 illustrates the multiclass classification results for the cross evaluated datasets. Figure 11: Cross-evaluation multiclass classification results of dataset. Figure 10: Binary classification results of dataset. Tables 10 and 11 show the multiclass classification results.