Description

Man in the middle (MITM) attacks can dramatically compromise the security of Wi-Fi network where an attacker eavesdrops and intercepts the communication medium over the wireless communication networks. This kind of attack aims to steal sensitive data such as credit card details, login accounts, and other important financial transactions. Even though that many detection techniques have been proposed to mitigate MITM attacks, however, this attack still occurs and causes tremendous damages. In this study, we propose a set of machine learning techniques to detect and identify MITM attacks on a wireless communication network. In addition, we evaluate and validate our approach based on the performance metrics, and compare the performance results with other machine learning techniques. SMART GRID is a new paradigm in the energy industry that has replaced the conventional electric grid. The smart grid harnesses the power of information technology to efficiently meet environmental criteria by facilitating the integration of green technologies and intelligently delivering electricity to clients via two-way communication. The addition of information and communications technology (ICT) services to the conventional electrical grid represents a technical advancement. It makes use of information interchange in both directions to construct an automated and widely dispersed system with extra features like real-time control, operational efficiency, grid resilience, and increased integration of renewable technology, all of which minimize carbon footprint.