# An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method
## 4 Results and Discussion
### ML Classification after Applying Feature Selection and Class Balancing Methods  
The feature selection using the ISSA method at first applied to all features that are 42 in number. The feature selection gives 14 multiclass important features, whereas it gives 15 feature subsets for binary class feature sets. Both selected feature subsets are given to the SMOTE-Tomek method for minority and majority class instances balancing, which is discussed in Section 3.3 regarding operational working and in Section 4.2, and the class-wise increment for both binary and multiclass data is discussed. However, these feature sets that are selected using the ISSA method and are class balanced using the SMOTE-Tomek method are fed to four ML classifiers. Both binary and multiclass classification results are shown in Table 4 and the graphical description is shown in Figure 5.  
The binary and multiclass classification results against extra tree classifiers showed the best performance, whereas previously it was best for random forest before applying feature selection and class balancing methods. However, there is no big difference between the random forest and extra tree classifier performance. The random forest showed 98.03% accuracy, precision, recall, and F1-score, which means it remains similar in all classes even in F1-score as well, which was lower in Table 3. The worst performing classifier again gradient descent boosting is with 94.35% accuracy, and almost similar in all other metrics as well. The best performer is the extra trees method with 98.09% accuracy and recall, and F-score with a slightly larger 98.10% precision score. The third-best performer method is the decision tree, which is nearer to the extra tree method. It showed 97.22% accuracy, precision, recall, and F1-score. If we look at multiclass classifiers' performance, the results are sufficiently increased as they increased in binary classification also. The binary class classification results as compared to Table 3 are increased from 96.46% to 98.09% in terms of accuracy. The best performer in multiclass classification is not changed this time as compared to Table 3. The best performer is the random forest with 85.15% accuracy and recall, 85.79% precision, and 85.31% F1-score. The second-best performer is an extra tree with 84.98%, which is nearer to 85%, whereas if we look at F1-score, which is 85.19%, that is, more importantly, good as compared to accuracy because this time this score is representing a class imbalance problem solution. Similarly, the third-best-performing classifier decision tree showed 83.81% accuracy with an increment of F1-score up to 84.07%. The worst classifier is again proven as a gradient descent-boosting method, which showed 82.01% accuracy, whereas F1-score is not increased in this case by comparing to the other three methods, but it improved as compared to Table 3 results. The graphical representation is shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Categories** & **Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Binary & Random Forest & 98.03 & 98.03 & 98.03 & 98.03 \\  & Extra Tree & 98.09 & 98.10 & 98.09 & 98.09 \\  & Decision Tree & 97.22 & 97.22 & 97.22 & 97.22 \\  & Gradient Descent Boost & 94.35 & 94.36 & 94.35 & 94.35 \\ Multiclass & Random Forest & 85.15 & 85.79 & 85.15 & 85.31 \\  & Extra Tree & 84.98 & 85.70 & 84.98 & 85.19 \\  & Decision Tree & 83.81 & 84.64 & 83.81 & 84.07 \\  & Gradient Descent Boost & 82.01 & 82.30 & 82.01 & 81.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Binary and multiclass classification results on original UNSW-NB15 dataset after applying feature selection and class balancing methods. However, if we look at classification results before and after applying feature selection and data balancing methods, the results of all classifiers improved for both types of classifications.