# An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method
## 6 Conclusions  
Network security becomes a worldwide problem and many studies and solutions have been proposed to solve such problems. These solutions still have a deficiency in them due to many current problems. These problems are with the NIDPS system itself, and also with the data mining domain. The development of NIDPS is mainly based on the training of a dataset to create a machine or deep learning domain classifier. This dataset is normally imbalanced due to the abundance of records of the normal flow of network traffic and has very network-attacked instances. In this way, the use of these imbalanced datasets to develop NIDPS will lead to compromising performance. Therefore, problems \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Year** & **Proposed Methods** & **Results** \\ \hline
[32] & 2019 & Proposed SVM with non-linear & Binary Accuracy = 85.99\% Multi-class \\  & & kernel behavior & Accuracy = 75.77\% \\
[40] & 2020 & OS- and SMOTE-based class & Accuracy = 77.16\% \\  & & balancing and CNN- and & \\  & & LSTM-based hybrid approach for & \\  & & classification & \\
[31] & 2020 & XGBoost-method-based feature & Multi-Class Accuracy = 77.51\%, \\  & & selection and ML classifiers for & Precision = 79.50\%, Recall = 77.53\%, \\
[26] & 2022 & Two-phase feature selection using & Multi-class Accuracy = 84.24\% \\  & & filter and wrapper methods with & \\
[39] & 2023 & XGBoost-method-based feature & Binary Accuracy = 87.07\%, \\  & & selection and LSTM-, RNN-, and & Multiclass Accuracy = 78.40\% \\  & & GRU-based classification & Binary Accuracy = 98.09\%, \\ Proposed & & ESA-method-based feature selection & Precision = 98.10\%, Recall = 98.09\%, \\ Framework & & and SMOTE-Tomek-based class & F1-score = 98.09\% \\  & & balancing for network intruder & F1-score = 98.09\% \\  & & detection & Multi class Accuracy = 85.15\%, \\  & & Precision = 85.79\%, Recall = 85.15\%, \\  & & F1-score = 85.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with recently applied different studies on binary and multiclass classification using UNSW-NB15 dataset. such as class imbalance and feature selection need to be solved first and then a NIDPS for network attack detection needs to be developed. The proposed study uses a framework in which feature set normalization is applied to class-imbalanced datasets (UNSW-NB15). Then the dynamic or automatic selection of features is applied using the ISSA method, whereas previous studies mostly used manual feature selection methods. The ISSA method is from the particle swarm optimization (PSO) domain in which a swarm moves to find the best or optimal solution. Therefore, ISSA methods are considered to be more intelligent as compared to other feature selection methods due to their searching potential nature. Previously applied methods of feature selection use a manual selection of features via hit and trial methods to enhance the NIDPS detection accuracy. However, the proposed study uses dynamic and searching-specific nature methods of feature selection for binary and multiclass feature sets separately. The selected features are then fed to the SMOTE-Tomek method of oversampling and under-sampling for class balancing. The class-balanced and feature selected feature-matrices are separated and finalized for binary and multiclass classifications. The results for binary and multiclass classifications are extracted using four different ML classifiers. These results are collected before and after applying feature selection and class balancing techniques. The results showed that if feature reduction is applied at first to reduce the feature dimensionality, and class balancing is applied to balance class instances, then the performance of binary and multiclass classification is improved. The automated and dynamic method of feature selection made a fully automated NIDPS. Furthermore, it reduces time and space complexity by excluding the less meaningful features. It leads to more efficient results detection with less time and space consumption.