## 2 Literature Review
During the training phase, independent feature drop and group feature drop techniques were used. The results showed that when employing the Random Forest (RF) classifier, the independent features approach performed better. The group feature drop, on the other hand, showed enhanced dropping patterns for the flooding attack. However, when using the Logistic Regression (LR) classifier, the drop patterns for both independent and group feature drops were constant and comparable. The results showed that the independent features strategy performed better when the Random Forest (RF) classifier was used. On the other hand, the flooding attack's group feature drop showed enhanced dropping patterns. The drop patterns for both independent and group feature drops were constant and similar when the Logistic Regression (LR) classifier was used, however. In [35], utilizing tree-based classification algorithms like Random Forest, XGBoost, LightGBM, and CatBoost, the researchers examined the effects of feature selection. The feature set of the AWID dataset was reduced by the authors from 155 to 15 features using the Shapley additive explanations (SHAP) approach. Their investigation findings showedwhich characteristics had the greatest impact on detection models. The features wlan.da, wlan.fc-subtype, and wlan.lc.ds were shown to be the most significant in the detection process. The AWID dataset's patterns of intrusion and attack were distinguished and identified with the use of these attributes. The results indicate the value of features in enhancing the performance of intrusion detection models and emphasize the effectiveness of feature selection based on tree-based classification methods. The authors were able to improve the accuracy and efficacy of the detection procedure by limiting the feature set and concentrating on the most useful qualities. Regarding a scalable ML-based intrusion detection system for IoT, the authors in [36] addressed the disadvantages of centralized IDS for devices with limited resources by utilizing two approaches--semi-distributed and distributed. The authors used feature selection approaches followed by classification in their investigation of the AWID dataset. The dataset was split into three parts, each of which has 68 features. Seven features were chosen from each dataset after performing feature selection, giving the full training set a total of 21 features. A semi-distributed strategy was used to find the most accurate feature selection technique for each dataset. The distribution method's classifier was also chosen to be a multi-layer perceptron (MLP) classifier. Experiments were used to determine the efficacy of the two proposed structures. Even with a substantial CPU time of 186.26 s, an excellent accuracy of 99.97% was attained using the semi-distributed technique. While retaining a detection accuracy of 97.80%, the distributed technique showed the lowest CPU time of 73.52 s. These outcomes show that both suggested topologies performed well in the tests that were conducted. The semi-distributed method demonstrated great accuracy, albeit requiring more CPU time. In contrast, the distributed technique reduced CPU time while preserving acceptable detection accuracy. The AWID dataset, which is the foundation of the wireless IDS literature and comprises a substantial collection of packets and its WEP-based infrastructure, has more than 150 different features. AWID has been enhanced, and AWID3 has been introduced by capturing and examining the traces of cyberattacks that were transmitted into the IEEE 802.1X extensible authentication protocol (EAP) environment. Our study seeks to fill important gaps and address tough challenges in-network security, particularly as it relates to IEEE 802.11 networks, based on the thorough literature review that has been provided. The literature already in existence has emphasized the rise of sophisticated attacks, including Krack and Kr00k, which pose serious risks to network integrity.