## 1 Introduction
The AWID dataset has become a popular experimental dataset in the field of wireless network environments. The \"curse of dimensionality\" problem is exacerbated by the large complexity and intrinsic duplication of this dataset. Designing effective intrusion detection strategies requires addressing these issues [22]. The large volume of the blind spot regions in the dimensionality dataset can lead to significantly variable estimates of actual model performance when algorithm designers use insufficient sample sizes to train and evaluate algorithms for finding patterns in a complex construct. Due to this variability, it is challenging to predict how well a model performs on data that has not yet been observed [23]. The imbalance between benign and attack samples might increase the false positive rate in addition to high dimensionality, which can influence the effectiveness of any suggested IDS [24]. The AWID dataset was released in 2016 [25]. It is the first dataset of its kind that focuses on IDS, more specifically on WIDS. Studying the AWID dataset will help researchers to be familiar with 802.11 network vulnerabilities and attacks, as well as educate them on the true effects of these attacks in daily life. The AWID dataset has 156 features and 37 million packets. A new version of this data was published in 2021 [26] by capturing and analyzing the traces of attacks that were sent into the IEEE 802.1X extensible authentication protocol (EAP) environment. It focuses on WPA2 Enterprise, 802.11w, and Wi-Fi 5. It includes multi-layer attacks like Krack and Kr00k. In the context of IEEE 802.11 networks, we make important advancements in the field of network security in this paper. First, we carry out a focused examination of the most current and important vulnerabilities, concentrating particularly on Krack and Kr00k attacks. Building upon this analysis, we develop an innovative and effective intrusion detection system (IDS) model using MATLAB. Our IDS model's main objective is to effectively identify and prevent Krack and Kr00k attempts to enhance network security. Our suggested model achieves excellent accuracy in identifying and mitigating targeted attacks by utilizing the strength of machine learning techniques. We evaluate the IDS model using the AWID3 dataset (real-world datasets) to validate the effectiveness of our method, showing strong evidence of its utility in protecting networks from developing threats. We also provide a thorough comparison analysis of the three models, highlighting the advantages and benefits that each model makes in identifying Krack and Kr00k attacks. Additionally, we demonstrate the superiority of our strategy in-network security by thoroughly contrasting the results of our research with those of other cutting-edge techniques. Finally, we investigate the potential for proactive security measures by utilizing machine learning's capabilities in detecting and preventing new attacks, opening the way for more robust IEEE 802.11 networks. The remainder of the paper is divided into the following sections: Section 2 offers a thorough analysis of the relevant work, examining research that has been undertaken in this area. The technique used in this study is presented in Section 3, along with the step-by-step process we used to create our IDS model. We show and discuss the findings from the experiments we conducted in Section 4, highlighting how well our IDS model performed in identifying Krack and Kr00k attacks. Finally, we conclude our research in Section 5, where we go over the consequences, restrictions, and possible directions for further study in IEEE 802.11 security. By arranging our work in this way, from the initial literature review to the concluding reflections on the significance of our findings, we hope to provide a thorough and coherent discussion of our research.