Detecting End-Point (EP) Man-In-The-Middle (MITM) Attack based on ARP
Analysis: A Machine Learning Approach
Abstract
End-Point (EP) Man-In-The-Middle (MITM) attack is a well-known threat in
computer security. It targets the data flow between endpoints, and the
confidentiality and integrity of the data itself. Several techniques
have been developed to address this kind of attack. With the current
emergence of machine learning (ML) models, we explore the possibility of
applying ML in EP MITM detection. Our detection technique is based on
address resolution protocol (ARP) analysis. The technique combines
signal processing and machine learning in detecting EP MITM attack. We
evaluated the accuracy of the proposed technique using linear-based ML
classification models. The technique proved itself to be efficient by
producing a detection accuracy of 99.72%.