Abstract
The Internet of Things (IoT) consists of a massive number of smart devices capable of data collection, storage, processing, and communication. The adoption of the IoT has brought about tremendous innovation opportunities in industries, homes, the environment, and businesses. However, the inherent vulnerabilities of the IoT have sparked concerns for wide adoption and applications. Unlike traditional information technology (I.T.) systems, the IoT environment is challenging to secure due to resource constraints, heterogeneity, and distributed nature of the smart devices. This makes it impossible to apply host-based prevention mechanisms such as anti-malware and anti-virus. These challenges and the nature of IoT applications call for a monitoring system such as anomaly detection both at device and network levels beyond the organisational boundary. This suggests an anomaly detection system is strongly positioned to secure IoT devices better than any other security mechanism. In this paper, we aim to provide an in-depth review of existing works in developing anomaly detection solutions using machine learning for protecting an IoT system. We also indicate that blockchain-based anomaly detection systems can collaboratively learn effective machine learning models to detect anomalies.
Highlights
The Internet of Things (IoT) consists of myriad smart devices capable of data collection, storage, processing, and communication
Our contributions are summarised as follows: first, we present the significance of anomaly detection in the IoT system (Section 2); we identify the challenges of applying anomaly detection to an IoT system (Section 3); after that, we describe the state-of-the-art machine learning techniques for detecting anomalies in the system (Section 4); we analyse the use of machine learning techniques for IoT anomaly detection (Section 5)
Machine learning has been applied for anomaly detection systems in I.T. and IoT systems
Summary
The IoT consists of myriad smart devices capable of data collection, storage, processing, and communication. It is challenging to secure IoT devices as they are heterogeneous, traditional security controls are not practical for these resource-constrained devices, and the distributed IoT networks fall out of the scope of perimeter security, and existing solutions such as the cloud suffer from centralisation and high delay. In recent years, using machine learning techniques to develop anomaly-based I.D.S.s to protect the IoT system has produced encouraging results as machine learning models are trained on normal and abnormal data and used to detect anomalies [1,2]. Running machine learning models can consume extensive resources, making it challenging to deploy such models on resource-constrained devices It requires massive data for training machine learning models to archive high accuracy in anomaly detection. This paper covers the federated learning technique that helps to collaboratively train effective machine learning models to detect anomalies (Section 4) and indicates that the use of blockchain for anomaly detection is a novel contribution as the inherent characteristics of a distributed ledger is an ideal solution to defeat adversarial learning systems (Section 5)
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