## 2 Related Work
### Feature Selection Techniques with Classification Algorithms  
The primary objective of feature selection aims to remove unimportant and redundant features from the original dataset while preserving its core information. [PERSON] et al. (2019) improved IDS performance by integrating information gain (IG) for feature selection with single classification using Multilayer Perceptron Neural Networks. Their approach was tested with the UNSW-NB15 dataset. In a different approach, [PERSON] (2018) and [PERSON] et al. (2019) proposed wrapper feature selection combined with individual classification for binary classification tasks. [PERSON] (2018) employed the Sigmoid Pigeon Optimization algorithm (SPIO) to select pertinent features and applied a three-layer CNN for classification, with the NSL-KDD dataset used for evaluation. [PERSON] et al. (2019) applied Pigeon-Inspired Optimization (PIO) for feature selection and evaluated their model using decision trees across the UNSW-NB15, NSL-KDD, and KDD99 datasets. Separately, [PERSON] et al. (2019) and [PERSON] et al. (2019) introduced embedded feature selection techniques using LightGBM, augmented by deep learning methods for classification. [PERSON] et al. (2019) applied the Denoising Autoencoder (DAE) and Variational Autoencoder (VAE) for binary classification using the NSL-KDD dataset, while [PERSON]. [PERSON] et al. (2019) employed Deep Neural Networks (DNNs) to perform both binary and multiclass classification tasks, utilizing the NSL-KDD, UNSW-NB15, and KDD99 datasets in their experiment. The fundamental concept of bootstrap aggregation, commonly known as bagging, aims to reduce variance error by aggregating the outputs of trained classifiers into a single prediction. [PERSON] et al. (2019) implemented a bagging ensemble methodology using decision trees to establish a new NIDS framework. They employed the Meth Flame optimization algorithm to identify the important effective features from the NSL-KDD dataset. [PERSON] et al. (2019) utilized a wrapper method on the same dataset in order to determine the essential features, employing a recursive feature elimination technique combined with the random forest algorithm for binary and multiclass classification tasks. [PERSON] and [PERSON] (2019) developed a wrapper approach termed TS-RF, utilizing the UNSW-NB15 dataset for multiclass classification analysis. Boosting is an effective strategy in machine learning to mitigate bias by sequentially training models and allowing subsequent models to correct prediction errors made by earlier ones. In the domain of IDSs, [PERSON] et al. (2019) and [PERSON] et al. (2019) have independently proposed boosting methods integrated with wrapper feature selection techniques. The model presented in ([PERSON] et al., 2019), named PSO-XGBoost, initially builds a classification model using XGBoost. Subsequently, Particle Swarm Optimization (PSO) dynamically explores and determines the optimal structure for XGBoost. Conversely, the framework developed by [PERSON] et al. (2019), termed IWOA-XGB, incorporates an enhanced Whale Optimization Algorithm (WOA) with XGBoost to enhance IDS performance. The research begins with establishing a classification model based on XGBoost, followed by an adaptive search using the WOA to optimize the XGBoost structure, thereby improving the model's efficacy in IDS scenarios. The NSL-KDD dataset was applied in ([PERSON] et al., 2019) to evaluate their models, which were designed to handle binary and multiclass classification tasks. In a similar approach, [PERSON] and [PERSON] (2019) applied the NSL-KDD dataset for their analysis. Their methodology incorporated a Hybrid Firefly and Black Hole Algorithm (HFBHA) to optimize XGBoost parameters. The HFBHA utilizes the gravitational pull of black holes to adjust star trajectories and employs a dual black hole technique with a secondary black hole.