Energy-aware dynamic Internet of Things security system based on Elliptic Curve Cryptography and Message Queue Telemetry Transport protocol for mitigating Replay attacks

Abstract

Security in the context of Internet of Things (IoT) is a critical challenge. The purpose of this work is to model and evaluate a dynamic IoT security system based on a generic IoT edge network in which nodes exchange messages through the Message Queue Telemetry Transport (MQTT) protocol. This work aims to increase MQTT security by mitigating data tampering, eavesdropping and Replay attacks by using the Elliptic Curve Cryptography (ECC), timestamps and wake up patterns, with the purpose of preserving node energy. The evaluated results will show that it is possible to increase the system lifetime by linking security levels and energy.

Introduction

The term IoT was born as a title of a presentation made by Kevin Ashton in 1999 at MIT [1]. IoT overlaps between Mobile Computing, Pervasive Computing, Wireless Sensor Networks and Cyber–Physical System. IoT can be defined as a wired or wireless network constituted by connected and unequivocally identified devices able to process data and communicate with each other with or without human help. Nowadays, the IoT is used in many fields for example: Logistic, Smart environment, Smart Agriculture, Smart cities etc. By the end of 2020 it is estimated that there will be approximately 30 billion connected devices with a data exchange greater than 40 Zettabytes [2]. These expectations raise many IoT challenges such as scalability, data volumes, self-organizing, data interpretation, interoperability, automatic discovery, software complexity, security and privacy, wireless communication. Since sensors typically have limited computing and storage capabilities, it is common to forward the generated data to a cloud computing platform for data processing and analysis, but the network latency and jitter can become significant. As a result, the low latency requirement of various delay-sensitive applications such as vehicular networks and smart health services cannot be met. To address the above issue, the Mobile Edge Computing (MEC) paradigm has emerged in recent years. MEC is an essential paradigm shift toward a hierarchical architecture and a more responsive design. As shown in Fig. 1, the edge is an intermediate computing layer between the cloud and end devices that complements the advantages of cloud computing by performing data processing at the edge of radio access networks of mobile telecommunications networks [3], [4]. Although the fog computing paradigm offers many benefits for different IoT applications, it faces many challenges such as scalability, complexity, dynamicity, heterogeneity, latency and security [5].

One of the critical requirements of supporting IoT applications in MEC is security, because in wireless communications the medium can be accessed by both authorized users and adversaries; consequently, this makes it vulnerable to many security attacks [6]. Edge computing and fog computing terms are used interchangeably in both academia and industry. Their main objectives are the same, but they differ on how they process and handle the data and where the intelligence and computing power are placed [7]. This work aims to increase the security of an IoT context at edge layer in which there are a certain number of lightweight nodes exchanging messages by using the MQTT protocol through a broker which is placed on a fog node. It is proposed to mitigate data tampering and eavesdropping by applying the ECC on the MQTT payloads. As a higher key-strength causes more energy consumption than a lower one, the used security level will be dynamically changed, and it will be linked to the node energy level in an attempt to increase the system lifetime. Obviously, a low security level key must be changed with high frequency for guaranteeing good security. A mechanism is also proposed to mitigate Replay attacks by adding a timestamp to the ciphered payloads and by using some wake up patterns to preserve lightweight nodes energy by decreasing the received replicated messages that must be dropped. A Replay attack can be detected by an IDS that is placed on each network node which controls the packet timestamp.