Anomaly Detection In IoT Devices Using Data Mining Techniques

Abstract

Internet of Things (IoT) devices are used to transmit data over a network. IoT device usage has grown and has been adopted in healthcare, smart homes, smart grids, connected cars and so on. However, IoT devices have security vulnerabilities and cannot provide a 100% guarantee of data privacy. They are prone to hacks and hardware Trojans that can lead to data theft. Hence there is a need to find ways to help solve the challenges of IoT devices. This paper proposes an approach that investigates how fraud, data theft, or fault, can be detected through the power profile of IoT devices in different operation modes using data mining techniques. Different cases will be made based on the behavior of the IoT device in terms of power consumption. These cases will be dependent on the mode of data transfer. Data mining techniques will be used to develop a model capable of detecting malicious behaviors and any form of the anomaly in power profiles of IoT devices.