Man-In-The-Middle Attack Detection Using Ensemble Learning

Cyber security is one of the most vital demands about network infrastructure and hence very necessary to protect the information sent and received during data transmission against outside factors trying to intercept systems. Man-In-The-Middle (MITM) attacks can dramatically compromise the security of wireless fidelity 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, many detection techniques have been proposed to mitigate Man-In-The-Middle attacks, however, this attack still occurs and causes tremendous damages. In this study, we propose a systematic approach using Ensemble Learning to detect MITM attack packets in a network. Our Ensemble Learning approach proves to be a valid stratagem for the detection of MITM Attack Packets.