A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Javed Asharf,Waqas Haider,Abdul Wahab,Nour Moustafa,Hasnat Khurshid,Essam Debie

doi:10.3390/electronics9071177

Abstract

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

Highlights

The recent development in communications and information technologies, such as the Internet of Things (IoT), has extraordinarily surpassed the traditional sensing of nearby environments.IoT technologies have facilitated the development of systems that can improve life quality
Explanation of vulnerabilities, threat dimensions and attack surfaces of IoT systems, including attack types related to IoT protocols, which are discussed in detail
Review of Machine Learning (ML)- and Deep Learning (DL)-based Intrusion Detection Systems (IDSs), involving their design choices, pros, cons and detection methods, which are covered in detail

Summary

Introduction

The recent development in communications and information technologies, such as the Internet of Things (IoT), has extraordinarily surpassed the traditional sensing of nearby environments.IoT technologies have facilitated the development of systems that can improve life quality. The recent development in communications and information technologies, such as the Internet of Things (IoT), has extraordinarily surpassed the traditional sensing of nearby environments. IoT is one of the fastest-growing technologies in computing, with an estimated 50 billion devices by the end of 2020 [1]. It has been estimated that, by the year 2025, the IoT and related applications have a potential economic impact of $3.9 trillion to $11.1 trillion per year [2]. The IoT devices can become smart objects by taking advantage of its core technologies like communication technologies, pervasive and ubiquitous computing, embedded devices, Internet protocols, sensor networks, and Artificial Intelligence (AI)-based applications [3]. The ubiquitous interconnection of physically distributed IoT devices extends the computation and communication to other IoT devices with different specifications [4]. Multiple types of sensors, embedded in these devices, enable them to gather real-time data from the physical devices remotely

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