# An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method
## 3 Materials and Methods  
The proposed methodology consists of three major steps (feature selection, class balancing, and classification), and all of these are shown in Figure 1. The proposed framework used the UNSW-NB15 dataset, which has a high data imbalance ratio between its classes. Therefore, a class imbalance is also a problem before applying binary and multiclass classifications. In our applied approach, firstly data preprocessing is employed, which scales data according to a standard format. The preprocessed data are furthermore given to an automated method of feature selection.  
The previous studies mostly used manual methods of feature selection such as fixing 15 features from 42 features of the UNSW-NB15 dataset based on their ranks or scores to feed ML classifiers. However, the proposed framework uses a dynamic method of feature selection instead of manual methods. Automated feature selection is a way in which all given features are fed to the method and select the most optimal ones. To employ this dynamic feature selection, the proposed study uses the ISSA method of feature selection. It works on a bio-inspired natural phenomenon of salps, which are sea species that remain in search of food optimally with their chain of followers.  
\begin{table}
\begin{tabular}{c c l l} \hline \hline
**Study** & **Year** & **Applied Methods** & **Dataset** & **Results** \\ \hline
[32] & 2019 & Proposed SVM with non-linear kernel behavior & UNSW-NB15 & Binary Accuracy = 85.99\% \\
[40] & 2020 & OSS- and SMOTE-based class balancing and CNN, & Multi-class Accuracy = 75.77\% \\  & & LSTM-based hybrid approach for classification & Accuracy = 77.16\% \\
[40] & 2020 & OSS- and SMOTE-based class balancing and CNN, & NSL-KDD & Accuracy = 83.58\% \\  & & LSTM-based hybrid approach for classification & & \\
[31] & 2020 & XGBoost-method-based feature selection and ML classifiers for classification & UNSW-NB15 & Binary Accuracy = 90.85\%, \\  & & selection and ML classifiers for classification & Precision = 80.33\%, \\
[31] & 2020 & XGBoost-method-based feature selection and ML classifiers for classification & & \\
[36] & 2021 & Manual selection of standard features and LSTM-based classification & UNSW-NB15 & Multi-class (3 Categories) Accuracy = 96.32\% \\
[26] & 2022 & Two phase feature selection using filter and wrapper methods with MLP classification & \\
[39] & 2023 & XGBoost-method-based feature selection and LSTM-, RNN-, & NSL-KDD & Binary Accuracy = 88.13\%, \\
[39] & 2023 & XGBoost-method-based feature selection and LSTM-, RNN-, & Multiclass Accuracy = 86.93\% \\
[39] & 2023 & XGBoost-method-based classification & UNSW-NB15 & Binary Accuracy = 87.07\%, \\  & & selection and LSTM-, RNN-, & Multiclass Accuracy = 78.40\% \\  & & GR-based classification & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tabular description of previously proposed NIDS studies.  
After the selection of features, the class imbalance problem is solved using the SMOTE-Tomek method. After solving the class imbalance problem, two types of results are discussed in this study. Firstly, the results of classification before applying to oversample, feature selection. Secondly, the results after applying oversampling method via SMOTE-Tomek. Both types of results are represented and discussed in Section 4, which gives strength to the applied methods.