Evaluating Machine Learning Methods for Intrusion Detection in IoT

Abstract

Cyber-attacks in IoT enabled devices have grown at an alarming rate since the start of the Covid-19 pandemic due to cyber physical digital transformation enabled through widespread deployment of low cost sensor embedded IoT devices in consumer and industrial IOT, as well as increase in computing power. Consequently, this adoption trend had led to 1.51 billion breaches on IoT devices during the first half of 2021 alone. This highlights the critical importance of being prepared for IoT vulnerabilities (IoT manufacturing and deployment sector) and attacks (malicious actors). In this respect machine learning (ML) especially deep learning (DL) strategies has emerged as the preferred methods to secure IoT devices from attacks. In this paper, we propose three deep learning algorithms for IoT intrusion detection based on mapping of IoT attacks to ML/DL methods. Our paper thus provides two contributions. First, we present a model that maps extant research on the application of ML/DL to specific IoT attacks. Second, through an optimal selection of the mapping, we present three algorithms (naïve Bayes, convolutional neural network and autoencoder) for detection of intrusion in IoT attacks. This provides a review of research opportunities and research gaps in the IoT IDS domain.