## 2 Literature Review
Four categories--binary and multiclass attacks, full features, and selected features--were used to categorize the experimental research. They used the extra trees (ET) approach to reduce the number of features in the AWID dataset to 26. The test set had 115,128 instances, which made up 20% of the AWID-CLS dataset, whereas the training set had 359,115 instances. The suggested model's binary classification accuracy was 98.6% on the validation data and 98.69% on the test data. The highest accuracy for multiclass classification was 98.47% on the validation data and 98.59% on the test data. Furthermore, the performance of the suggested model showed increased accuracy outcomes when the number of attributes was decreased. The authors' high precision percentage for binary classification was 99.67% on the validation data and 99.66% on the test data. Similar results were obtained for multiclass classification, where 99.78% of the validation data and 99.77% of the test data were correctly classified. These results demonstrate the efficiency of the suggested wireless IDS system by demonstrating its precision in classifying and identifying various attack types. Deep neural networks and feature extraction were combined, and the accuracy significantly increased, especially when the number of characteristics was decreased. In [32], the authors addressed the imbalanced and high-dimensional network traffic issues with a system for intrusion detection. To improve classification accuracy, the suggested system makes use of feature selection and ensemble learning approaches. In this respect, the authors devised a hybrid strategy that combines the bat algorithm (BA) and correlation-based feature selection (CFS), which maximizes the effectiveness of the feature selection process and enhances classification accuracy. The authors applied their suggested model to the NSL-KDD, AWID-CLS-R-Tst, and CIC-IDS2017 datasets to assess its efficacy. The AWID-CLS-R-Tst dataset underwent several preprocessing processes. This required both the replacement of missing values with zeros and the filtering out of features with constant values. The original 155 features were consequently. Condensed to 84 features, an ensemble classifier was used to build the classification model. The superiority of the suggested strategy was shown by the experimental findings. Regarding the AWID dataset specifically, a subset of just eight chosen attributes was used to obtain a remarkable accuracy rate of 99.52%. In contrast, the accuracy was 98.2% when feature selection was not used. Hence, many kinds of literature studied the impact of network intrusion detection systems; [PERSON]. [PERSON] et al. [33] mentioned in their research both wireless and wired intrusion detection systems using the AWID dataset, which includes wireless network attacks, and the UNSW-NB15 dataset, which consists of a wired network attack. In addition, they focused on the other performance matrices that are critical in intrusion detection systems such as F1, recall, and precision. After they preprocessed the datasets, they remedied the imbalance problem via SMOTET (synthetic minority over-sampling technique), the feature selection was performed using XGBoost, and finally, Bayesian optimization was applied before applying different ML algorithms. [PERSON] [34] attempted to address the issue of dataset imbalance and enhance the performance measures of machine learning methods applied to intrusion detection in his study. The AWID-CLS-F-Trn and AWID-CLS-R-Trn subsets of the AWID dataset were the subject of the investigation. These subsets represent various techniques for classifying assaults and actual attacks, respectively. It is noteworthy that the AWID dataset comprises two identical datasets that only vary in labeling strategies. [PERSON] used feature selection (FS) approaches and included all features in the analysis to improve the performance metrics. After that, the collected findings were contrasted, paying close attention to flooding and impersonation attacks.