Detection and prevention of man-in-the-middle attack in iot network using regression modeling

Abstract

Security is the primary concern in any IoT application or network. Due to the rapid increase in the usage of IoT devices, data privacy becomes one of the most challenging issue to the researcher. In IoT applications, such as health care, smart homes or any wearables, transmission of human's personal data is more frequent. Man-in-the-Middle attack is one in which outsiders eavesdrops the communication between two trusted parties and steal the important information such as password, personal identification number, etc., and misuse it. So, this paper proposes a Regression Modelling technique to detect and mitigate the attack to provide attack-free path from source to destination in an IoT network. Three machine learning techniques Linear Regression (LR), Multi-variate Linear Regression (MLR) and Gaussian Process Regression (GPR) used and performance of these three algorithms analyzed on various metrics and shown Gaussian Process Regression provide higher rate for detecting the attacks and produces the lower rate for misclassification of attacks.