## 4 Results and Discussion
### Performance of Machine and Deep Learning Models
In literature, different techniques have been by researchers that are generally applied on the terrestrial networks and are not appropriate for satellite networks because of different reasons such as limited resources, different tolerance for attacks, limited computational power, and scarcity of datasets for satellite networks. Although the performance of other models is degraded when used with STIN datasets, the proposed RFMLP still performs better as shown in Figure 3. The proposed model is validated using the modern and famous network attack dataset UNSW-NB15. The accuracy score with the UNSW-NB15 dataset is 99.94% which is better than the state-of-the-art approaches [41]. The best performance on these intrusion detection datasets shows the superiority, significance, and reliability of the proposed model. Figure 4 presents the performance comparison of all models in terms of accuracy on KDD-CUP 99 and NSL-KDD datasets which are the most commonly used datasets to validate the performance of intrusion detection approaches. It can be seen that the result of the proposed RFMLP surpassed the performance of other models. RFMLP shows accurate results for each category of attack including DoS, Probe, R2L, and U2R on the KDD-CUP 99 dataset and proves its good generalization ability. In addition to conventional performance metrics of accuracy, precision, recall, and F1 score, Mathews correlation coefficient (MCC) has been computed for RFMLP. The proposed model has shown a 0.99 score for MCC, which proves its robustness. Furthermore, the receiver operating curve (ROC) has been drawn for the proposed model. Figure 5 illustrates the ROC curve of the proposed model on KDD-C \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attack Type** & **RF** & **SVM** & **LR** & **MLP** & **RFMLP** \\ \hline Backdoor & 88.14 & 85.22 & 85.48 & 88.17 & 96.67 \\ LDAP DDoS & 86.11 & 84.45 & 84.35 & 91.21 & 93.14 \\ MSSQL DDoS & 88.45 & 86.22 & 87.16 & 88.36 & 95.24 \\ NetBIO DDoS & 88.45 & 86.34 & 86.79 & 90.41 & 96.65 \\ Portmap DDoS & 87.32 & 79.99 & 81.01 & 89.17 & 91.21 \\ Syn DDoS & 88.21 & 82.38 & 82.01 & 92.34 & 97.25 \\ UDP DDoS & 85.99 & 84.18 & 84.17 & 89.22 & 97.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy of classifiers on STIN terrestrial dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{3}{*}{RF} & KDD-CUP 99 & 98.92 & 98.28 & 97.65 & 97.96 \\  & NSL-KDD & 97.39 & 97.62 & 97.45 & 97.53 \\  & STIN & 87.75 & 89.98 & 91.02 & 90.50 \\ \hline \multirow{3}{*}{SVM} & KDD-CUP 99 & 98.56 & 99.62 & 99.62 & 99.62 \\  & NSL-KDD & 97.53 & 97.78 & 97.62 & 97.70 \\  & STIN & 84.24 & 83.35 & 85.62 & 84.48 \\ \hline \multirow{3}{*}{LR} & KDD-CUP 99 & 97.85 & 97.13 & 97.65 & 97.39 \\  & NSL-KDD & 94.39 & 96.61 & 95.82 & 96.21 \\  & STIN & 84.44 & 82.18 & 81.99 & 82.08 \\ \hline \multirow{3}{*}{MLP} & KDD-CUP 99 & 98.94 & 98.99 & 99.21 & 98.65 \\  & NSL-KDD & 99.83 & 99.62 & 99.82 & 99.72 \\  & STIN & 89.24 & 88.28 & 92.67 & 90.47 \\ \hline \multirow{3}{*}{RFMLP} & KDD-CUP 99 & 99.99 & 99.99 & 100.0 & 99.99 \\  & NSL-KDD & 99.99 & 99.99 & 99.99 & 99.99 \\  & STIN & 96.24 & 94.28 & 98.67 & 96.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average Accuracy of classifiers on all three dataset. datasets. The ROC curve also shows the superior performance of the proposed model on all three datasets.