## 2 Related Works  
Machine learning models often incorporate data enhancement techniques, such as feature selection, Auto Encoders, and data balancing, to improve the accuracy of intrusion detection, as shown in Table 1.  
[PERSON] et al. [4] employed Random Forest as the optimal classifier and utilized a hybrid feature selection method that combines Mutual Information and Random Forest Gini impurity to enhance intrusion detection accuracy in the CIC-IDS2017 dataset [8]. [PERSON] et al. [5] utilized Random Forest as the classifier and Boruta as the feature selection method to identify diverse network attacks in the NSL-KDD dataset. [PERSON] et al. [6] \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Classifier** & **Enhancement** & **Dataset** \\ \hline [PERSON] et al. [4] & Random Forest & \begin{tabular}{c} Mutual Information, \\ Random Forest Gini impurity \\ \end{tabular} & CIC-IDS2017 \\ \hline [PERSON] et al. [5] & Random Forest & \begin{tabular}{c} Boruta Feature Selection \\ \end{tabular} & NSL-KDD \\ \hline [PERSON] et al. [6] & XGBoost & \begin{tabular}{c} Salp Swarm \\ algorithm \\ \end{tabular} & \begin{tabular}{c} NSL-KDD \\ UNSW-NB15 \\ \end{tabular} \\ \hline [PERSON] et al. [7] & Logistic Regression & Logistic Regression & NSL-KDD \\ \hline [PERSON] et al. [11] & DNN & Auto Encoder & \begin{tabular}{c} CIC-IDS2017 \\ UNSW-NB15 \\ \end{tabular} \\ \hline [PERSON] et al. [12] & DNN & Auto Encoder & \begin{tabular}{c} NSL-KDD \\ UNSW-NB15 \\ \end{tabular} \\ \hline [PERSON] et al. [13] & DNN & Auto Encoder & \begin{tabular}{c} CIC-IDS2017 \\ NSL-KDD \\ \end{tabular} \\ \hline [PERSON] et al. [14] & DNN & Auto Encoder & \begin{tabular}{c} ISCX-IDS-2012 \\ UNSW2018 \\ \end{tabular} \\ \hline [PERSON] et al. [15] & J48 & \begin{tabular}{c} Manual Feature Selection \\ \end{tabular} & AWID \\ \hline [PERSON] et al. [16] & Semi-boosted Tree & Gini Impurity & AWID \\ \hline [PERSON] et al. [17] & DNN & \begin{tabular}{c} Auto Encoder, three \\ feature selection \\ methods \\ \end{tabular} & AWID \\ \hline [PERSON] et al. [18] & \begin{tabular}{c} Support Vector Machines (SVM) \\ \end{tabular} & \
\begin{tabular}{c} Feature selection, \\ data balancing, Auto Encoder \\ \end{tabular} & AWID \\ \hline [PERSON] et al. [19] & Logistic Regression & Auto Encoder & AWID \\ \hline [PERSON] et al. [20] &
\begin{tabular}{c} Reinforcement Learning \\ \end{tabular} & Auto Encoder & AWID \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Machine learning models incorporate data enhancement techniques for intrusion detection. employed XGBoost as the classifier and the Salp Swarm algorithm for feature selection by using both the NSL-KDD [9] and UNSW-NB15 [10] datasets. [PERSON] et al. [7] adopted logistic regression for feature selection by using the NSL-KDD dataset. Deep neural networks (DNNs) have become an effective classifier in intrusion detection models and have demonstrated superior accuracy compared to machine learning-based models for various attack types across different datasets. Auto Encoder has emerged as a popular data augmentation technique for enhancing DNN performance. Several studies [11; 12; 13; 14] have successfully utilized Auto Encoder in conjunction with DNN for intrusion detection in datasets such as CIS-IDS2017, NSL-KDD, and UNSW-NB15. In an effort to detect the three distinct AWID wireless attacks, [PERSON] et al. [15] employed manual feature selection and identified J48 as the most effective classifier. [PERSON] et al. [16] utilized Gini impurity as the feature selection method and a semi-boosted tree as the classifier. [PERSON] et al. [17] also utilized DNN with an Auto Encoder and proposed three feature selection methods with data balancing. D-FES achieved exceptionally high accuracy for impersonation attacks but did not detect flooding or injection attacks. [PERSON] et al. [18] employed Support Vector Machines (SVM) with feature selection, Auto Encoder, and data balancing, demonstrating superior binary detection for the three AWID attacks.