## 3 Methodology  
This chapter outlines the process for implementing the framework for intrusion detection in wireless networks using machine learning techniques.  
Wireless technologies have increased rapidly in recent years. While serious efforts have been made to secure these technologies, most security measures have proven inadequate in practice. The AWID project aims to provide a solid basis for researchers to develop robust security mechanisms for current and future generations of wireless networks by providing tools, methodologies, and datasets, as the previous datasets were not specific to wireless networks.  
Wi-Fi (IEEE 802.11) has taken over as the standardized technology for connecting digital devices in wireless local area networks due to the rise of smart portable devices such as smartphones, tablets, and Internet of Things (IoT) devices. Wi-Fi is frequently used in critical locations as well as in homes, businesses, and organizations. Unsurprisingly, extensive academic research has focused on 802.11 protocol security as well as Wi-Fi network security. With frequent modifications and corrective actions, vulnerabilities have been found in even the most recent versions of the software although these vulnerabilitieshave existed for more than 20 years. Security in wireless technology is a major issue that has long remained unresolved. External security measures should therefore be used as crucial elements of 802.11 wireless networks for defending against known or unknown attacks [26].  
The AWID dataset was extracted in 2016, and then it was developed into a new version in 2021 called AWID3. The main differences between the old version and the new one can be summarized as follows:  
1. AWID3 includes recently identified attacks against the 802.11 protocol, including well-known instances like Krack and Kr00k. This inclusion enables researchers to investigate and create practical defenses against these particular dangers within the framework of the dataset.
2. A network's packet-level details are contained in the PCAP format used to supply the data in AWID3. Researchers now have access to extensive data that can be utilized to assess network features and meet specific research objectives. The dataset also includes the pairwise master key (PMK) and TLS keys.
3. The enterprise versions of the 802.11 standards are the main emphasis of AWID3. Stronger security features, like support for alternative network architectures and the use of protected management frames (PMF), which were introduced with the 802.11 w revision, are often present in these versions. By focusing on enterprise versions, the dataset is more applicable to actual security issues.
4. The link layer of the 802.11 protocol is initially targeted via attacks on the AWID3 dataset. These assaults, nevertheless, quickly spread to higher layers, affecting protocols that run at different levels of the network stack. Researchers can examine the interrelated nature of network vulnerabilities due to this comprehensive perspective of attack propagation.
5. Every scenario in the dataset is covered in detail by AWID3. Researchers may undertake detailed analysis and evaluation with the help of this documentation, which also helps them grasp the nuances of assault scenarios.