Detection and prevention of man-in-the-middle attack in iot network using regression modeling. (July 2022) - Sivasankari, N.; Kamalakkannan, S.
Highlights: Simulating an IoT nodes and generating both normal and adversary (MitM) data traffic using NS2 tool. Three machine techniques such as linear regression (LR), multilinear regression (MLR) and gaussian process regression (GPR) applied to collected data set. Performance of techniques analyzed with both positive and negative measures. Proved that gaussian process regression technique provides greater accuracy in detecting the attack while identifying the path between the source and the destination. To enhance privacy in IoT network by detecting Man-in-the-Middle attack. No need of central controller to detect the attack. Hence, each source node in IoT LAN finds attack free path (without MitM node) to the destination node on its own. Provides a higher detection rate of attack and lower false measures. 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.