## 2 Review of Existing Work
### Intrusion Detection with NSL-KDD Dataset  
Existing studies have utilized several methods, such as ML algorithms [9; 10], swarm-intelligence and ANN (Artificial Neural Network), for detecting intrusions. Accordingly, [PERSON]. [11] suggested a feature selection method using a GA (Genetic Algorithm), wherein the GA finds the ideal features from an NSL-KDD dataset. Furthermore, hybrid classifiers have been used, encompassing LR (Logistic Regression) and DT (Decision Tree) to achieve the ideal accuracy and detection rate. The study has also employed and compared the performances of varied meta-heuristic methods in optimizing the chosen ideal features. The empirical outcomes reveal that the GWO (Grey Wolf Optimization) method showed a better accuracy rate of 99.44%. Moreover, the integration of several feature-selection-based approaches and the ML methods has been pursued for efficient intrusion detection. Initially, the most relevant features were retrieved with the use of the hybrid meta-heuristic algorithm, and later, supervised ML methods were employed for detecting various attacks with a satisfactory accuracy rate. Accordingly, the NSL-KDD dataset was used with the suggested AdaBoost model. A cost-sensitive classifier has been employed to enhance the accuracy of the minority class, affording an 81.1% overall accuracy [12]. Further, [PERSON]. [13] considered a method relying on the HOA (Horse Herd Optimization Algorithm) to detect network intrusions. To perform the classification, KNN (K-Nearest Neighbour) was employed. A suitable performance was secured. To improve the performance further, [PERSON]. [14] employed a hybrid meta-heuristic approach with an OWKELM (Optimal Wavelet Kernel Extreme Learning Machine)-based classifier. The suggested system included a hybrid MFO (Moth Flame Optimization) with HC (Hill Climbing)-based process for feature selection. Extensive simulations have been undertaken on the NSL-KDD dataset, and the better performance of the suggested model was revealed. Besides this, intrusion detection has been performed in several applications using the NSL-KDD dataset [15]. For instance, Ref. [16] set out a model to detect and classify IoT network intrusions for use in agriculture. NSL-KDD was employed as the input dataset. Feature extraction was performed with PCA (Principal Component Analysis). For the classification of the dataset, ML methods such as SVM (Support Vector Machine), RF (Random Forest) and LR (Linear Regression) have been employed. The performances of the suggested ML classifiers have been assessed. The accuracy rate was found to be 98% when using SVM, 78% when employing LR and 85% when using RF. Contrarily, Ref. [17] utilized an optimized strategy based on GB-EGWO (Genetic-Based-Enhanced Grey Wolf Optimization) to detect intrusions. The simulation outcomes confirm the better performance of the suggested system. Likewise, [PERSON]. [18] employed a bio-inspired method, namely, GWO, to improve its effectiveness in detecting anomalous and normal traffic. Additionally, the ELM (Extreme Learning Machine) has been considered for use as a classifier, and modified GWO has been used for tuning the parameters of ELM. The accuracy rate has been found to be 81%. Following this, Ref. [19] considered EBGOW (Enhanced Binary Grey Wolf Optimization) to perform feature selection by balancing the parameters so as to detect anomalies. Evaluations of this method have been performed on the NSL-KDD dataset, containing varied attack classes, in comparison to other benchmark methods such as Binary PSO, Binary Bat Algorithm, and four variants of GWO, in performing feature selection. The empirical outcomes show that EBGOW achieved a better performance than supplementary classifiers, with a classification rate of 87.46%. To improve the performance rate further, [PERSON]. [20] suggested using GSO (Glow-worm Swarm Optimization) with PCA (Principal Component Analysis). The detection rate was 94.08%.