A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks

The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions.