## 2 Literature Review  
The evolution of communication networks has resulted in an exponential rise in the number of Internet of Things (IoT) devices that are connected to Wi-Fi networks. These devices generate enormous amounts of data traffic, which can be malicious, and makes it difficult to detect such attacks. Feature selection is used to reduce the amount of data for intrusion detection model classifiers by removing noisy information and choosing the best features in the data, which participates in improving the IDS performance and solving these challenges. We will focus on the AWID dataset, a Wi-Fi network intrusion benchmark dataset introduced due to the lack of a dataset in wireless intrusion detection systems (WIDSs) where the oldest datasets were about IDSs in general [27]. Moreover, this dataset was the first dataset produced using wireless network traffic. AWID has been enhanced and extended to AWID3 by capturing and examining the traces of cyberattacks sent into the IEEE 802.1X extensible authentication protocol (EAP) environment. It concentrates on 5G wireless networks, 802.11 w, and WPA2 Enterprise. It includes multi-layer and modern attacks like Krack and Kr00k [26]. This section will present some previous studies that used the AWID dataset in their research. In Ref. [28], the authors present a novel method that combines stacked feature extraction with weighted feature selection using deep learning techniques. This is a ground-breaking strategy for a Wi-Fi impersonation detection attack. The goal is to increase the detection of such attacks' precision and effectiveness. Three distinct algorithms--ANN, C4.5, and SVM--were used for the experiments and were developed and assessed using the AWID datasets as the basis. By utilizing this method, the authors were able to identify and detect Wi-Fi impersonation assaults with an astounding accuracy rate of 99.918%. This represents an important development in the area and illustrates the effectiveness and potential of the suggested deep-feature extraction and selection strategy. The results of this study emphasize the significance of using sophisticated methodologies and utilizing extensive datasets to address the constantly changing problems caused by Wi-Fi impersonation attacks. The achieved accuracy rate underscores the value of ongoing research and development in increasing Wi-Fi security in addition to demonstrating the usefulness of the suggested approach. [PERSON] et al. [29] used deep learning algorithms to identify and analyze critical attacks and threats on Internet of Things devices, particularly those that take advantage of weaknesses in wireless communications. They have achieved a high accuracy with 99.91% for the ISCX dataset and 98.22% for the AWID dataset. In [30], the authors built a wireless intrusion detection system (IDS) designed specifically to operate access points in passive mode. The complex nature of wireless attacks, which frequently include the deceitful fabrication of fake access points to dupe unwary users, served as the inspiration for this strategy. The proposed IDS sought to efficiently detect and counteract such malicious activity by concentrating on the passive mode. When experimenting with the AWID dataset, the approach proposed by the authors produced encouraging results, obtaining an exceptional accuracy rate of 98%. These results demonstrate how well the suggested method works for precisely recognizing and thwarting wireless attacks. The IDS demonstrated its capacity to distinguish between legal and fraudulent access points by utilizing the passive mode and applying complex detection algorithms. [PERSON] et al. [31] proposed a feed-forward deep neural network and feature extraction-based wireless intrusion detection system (IDS). Two datasets, UNSW-NB15 and AWID, were used to assess the system's performance. They also contrasted their findings with those of well-known machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), and k-Nearest Neighbor (kNN).