# An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method
## 2 Related Work
The reported results show that accuracy improvement for multiclass and binary are reached up to 87.44% and 98.14%, respectively. The recursive elimination method with random forest method was adopted for features selection where many of the classical ML methods were applied on the UNSW-NB15 dataset [35]. The two-stage classification was performed where 74% accuracy was achieved at the first stage with SVM and then its output was given to multiple classifiers and achieved the 86% classification accuracy with 12% improvement. [PERSON] et al. [36] proposed UNSW-NB15 dataset classification and BoT-IoT dataset classification on binary and three classes (DoS, DDoS, and Normal) based multiclass classification. In this method, the authors reported that standard features are used, which are selected as per protocol followed by networks. The manual selection to cover class imbalance is performed by selecting equal instances of binary and multiclass classification. An LSTM model for classification is used where 96.3% accuracy is achieved on both types of classification. An efficient technique to propose IDS was conducted by [PERSON] et al. [37]. It uses a feature selection approach using four filters, chi-square, symmetric uncertainty, and an automated method of the stacked encoder. These filters formed a new feature vector, which fed the ensemble method of classification having LSTM, SVM, XGBoost, and FLN methods. Further optimization of these ensemble methods is performed using the crow search method. It validated the results on three datasets; namely, NSL-KDD, Kyoto, and CSE-CIC-IDS-2018. Another study [38] used SMOTE method to over-sample the minority class instances and increase their class ratio against the normal class using three datasets: NSL-KDD, KDD99, and UNSW-NB15. The features are selected using the Gini method, where only 24 features are selected among 41 in the KDD-99 dataset, 20 selected from NSL-KDD, and 30 from the UNSW-NB15 dataset. The weighted F1-scores reported for three datasets, the highest weighted F1-scores for UNSW-NB15 dataset achieved by random forest and it increased to 78% via SMOTE as compared to the original UNSW-NB15 F1-score that was 77% for multiclass. The highest binary F1-score for SMOTE-based UNSW-NB15 against random forest was up to 88.5%. The KDD99 achieved 94% F1-score for binary class and 90 with decision tree as highest for multiclass. NSL with SMOTE achieved 72 for multiclass with random forest and 83 highest for binary class with K-means. The multiple deep learning models have been applied on two datasets: NSL-KDD and UNSW-NB15 [39]. The XGboost method of feature selection was applied to reduce the feature space. The 22 and 17 numbers of the feature have been selected from NSL-KDD and UNSW-NB15 datasets, respectively. The applied methods include simple Recurrent Neural Network, LSTM, and GRU models. Both binary and multiclass types of classifications are applied, where XGBoost-LSTM achieved the highest accuracy in binary classification for the NSL-KDD dataset, and test accuracy as 88.13% and 87.07% for the UNSW-NB15 dataset. In multiclass classification, NSL-KDD achieved 86.93% and for the UNSW-NB15 dataset, and it reached up to 78.40% via the XGBoost-GRU method. The two-way minority balancing and majority creation are applied with SMOTE and One-Sided Selection (OSS) methods [40]. In this way, a balanced dataset is produced that reduces the training and testing time in the next stage. CNN and LSTM classifiers are applied to extract the spatial and temporal features, respectively. The testing accuracy for NSL-KDD% was reported as 83.58% and 77.16% for the UNSW-NB15 dataset. It mainly focuses on hybrid approaches applied for class balancing and NIDS.