Man in the Middle Attack Detection for MQTT based IoT devices using different Machine Learning Algorithms

Abstract

The usage of appropriate data communication protocols is critical for long-term Internet of Things (IoT) implementation and operation. The publish/subscribe-based Message Queuing Telemetry Transport (MQTT) protocol is widely used in the IoT world. Cyber threats on devices and networks using MQTT protocols are expected to rise with the protocol's growing popularity among IoT manufacturers. Among these threats is the man in the middle (MiTM) threat, in which an attacker listens in on or modifies traffic between two parties by intercepting conversations between them. In this paper we have implemented five different machine learning model on an open-source dataset and evaluated different parameters like accuracy, precision, recall, F1 score and most importantly training time and test time because most of IoT network are hosted on resource constrained devices like Raspberry Pi.