MDSClone: Multidimensional Scaling Aided Clone Detection in Internet of Things

Abstract

MDSClone: Multidimensional Scaling Aided Clone Detection in Internet of Things is a very serious threat on the Internet of Things (IoT), owing to the simplicity of an attacker gathering configuration and authentication credentials from a non-tamper-proof node and replicating it in the network. In this article, we propose MDSClone, a novel multidimensional scaling (MDS) clone detection method.

The thorough analytical and experimental assessments of MDSClone demonstrate a 100 percent probability of clone detection. In addition, we are proposing several modifications to the original MDS calculation, which in large scale scenarios lead to a speed of more than 75 %. MDSClone’s demonstrated efficiency proves to be a promising method towards a practical clone detection design in IoT.

Introduction

MDSClone: Multidimensional Scaling Aided Clone Detection in Internet of Things (IoT) is an emerging networking paradigm in which a large number of interconnected devices communicate with each other to facilitate communication between people and objects. For example, a smart city consists of several smart sectors, such as smart homes, smart hospitals, and smart cars, which are significant IoT applications. Each IoT gadget is equipped with embedded sensors and wireless communication capabilities in a smart home scenario.

The sensors can collect information about the environment and communicate with each other, as well as with the house owner and a central monitoring system. Patients wear implantable sensors that collect body signals and send the data to a local or remote database for further analysis in a smart hospital scenario that could be implemented using body sensor networks (BSN). As another example, sensors embedded in cars can detect accident events or traffic information and exchange such information collaboratively in a smart traffic scenario.