## 2 Related Work
### Literature Review of Feature Selection Methods for Intrusion Detection Systems
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Year** & **Proposed Method** & **Goal/Success** \\ \hline [PERSON] et al. [21] & 2010 & A novel feature selection method for KDD & The performance was increased with less features \\ \hline [PERSON] et al. [11] & 2011 & A technique for selecting features that used the mutual information measure & A higher accuracy specifically for U2R and R2L attacks \\ \hline [PERSON] et al. [22] & 2012 & A wireless IDS that used filter and wrapper approaches & The number of features was reduced from 38 to 8 with higher accuracy \\ \hline [PERSON] and [PERSON] [23] & 2017 & A feature selection approach that combined binary gravitational search and mutual information techniques in a hybrid manner & The proposed method increased the detection and accuracy rates while decreasing the false-positive rates \\ \hline [PERSON] et al. [24] & 2017 & Wi-fi IDS that used a weighted feature selection technique and neural network algorithm & The proposed method could handle unknown attacks and outperformed the state-of-the-art methods \\ \hline [PERSON] et al. [25] & 2018 & Evaluated different machine learning techniques for IDSs & The performance was increased when C4.5, SVM, neural network, and fuzzy association rules were used \\ \hline [PERSON] et al. [26] & 2019 & A feature selection and clustering algorithm which used wrapper and filter approaches & The proposed method produced higher detection and accuracy rates than leading methods in the literature \\ \hline [PERSON] et al. [27] & 2020 & A feature grouping and selection technique which used deep learning auto-encoder IDS & The presented method outperformed the state-of-the-art methods in terms of detection accuracy, ease of training, and adaptability \\ \hline [PERSON] et al. [28] & 2020 & A feature selection process and ensemble learning techniques & The presented CFS-BA feature selection algorithm outperformed state-of-the-art approaches based on various metrics \\ \hline [PERSON] et al. [29] & 2020 & A wireless sensor networks used dynamic feature selection and fuzzy temporal decision tree classification & The proposed method effectively detected known and unknown intrusions with less energy consumption \\ \hline [PERSON] and [PERSON] [30] & 2021 & A TS-RF that used a wrapper-based feature selection method & The proposed feature selection method improved the accuracy while decreasing the false-positive rates \\ \hline [PERSON] et al. [31] & 2021 & A hybrid approach selected the best subset of features & The proposed method generated high accuracy and outperformed other state-of-the-art methods \\ \hline [PERSON] et al. [32] & 2021 & An ensemble feature selection and classification on the cloud environment & A model performance was increased when it was compared with existing methods \\ \hline [PERSON] et al. [33] & 2022 & Metaheuristics-based feature selection model for IDSs & It identified attacks with fewer features \\ \hline [PERSON] et al. [34] & 2022 & Multi-level correlation-based feature selection method analyzed on the UNSW-NB’15 dataset & Its superiority over existing techniques \\ \hline [PERSON] et al. [35] & 2022 & Two entropy-based techniques IG and GR & Achieved 99.98\% accuracy with fewer features \\ \hline [PERSON] et al. [36] & 2023 & A hybrid heuristics artificial feature selection method & It outperformed existing methods with higher accuracy \\ \hline [PERSON] and [PERSON] [37] & 2023 & Multi-objective feature selection in IoT networks & The performance was increased significantly \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Current feature selection methods that are used in the literature for intrusion detection systems.