A Comparative Study on Vulnerabilities, Challenges, and Security Measures in Wireless Network Security

Featuring a focus on machine learning algorithms and the crucial field of wireless network security, this research project emphasizes the strategic usage of honeypots through a variety of aims. Study’s core aim is to acknowledge and address implications of wireless network security by employing comparative machine learning algorithms. Through strategic deployment of honeypots, custom-tailored for detecting and preventing security vulnerabilities, project endeavors to mitigate potential threats in wireless networks. Dataset, generated using Tpot within Debian operating system, serves as the cornerstone of this research, with data collection spanning from August 2023 to September 2023, ensuring its timeliness and relevance. With approximately 60,314 records across 14 columns, this dataset presents a substantial and comprehensive resource for investigating wireless network security vulnerabilities. Careful attention has been given to inclusion and exclusion criteria during the dataset construction process to align it closely with specific research objectives. Overarching goal of this research project is to enhance our understanding of complexities in wireless network security and explore how machine learning algorithms, specifically those leveraging honeypots, can effectively address these vulnerabilities and elevate the overall security of wireless networks.