An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review

Devices connected to the Internet of Things are expanding quickly and producing tons of data. Simultaneously, malicious attempts to gain access to sensitive data or disrupt networks have accelerated and grown more sophisticated. As a result, cybersecurity has emerged as a critical matter in the evolution of prospective networks capable of responding to and countering such threats. Intrusions detecting systems (IDS) are crucial for IoT security. There are two types of intrusion detection systems: signature-based and anomaly-based. Signature-based work to identify known attacks. However, it will be unrealistic to rely on pre-defined threat monitoring (signature-based) due to the variety of attack types in addition to the unstructured data generated by IoT devices and their characteristics. On the other hand, anomaly-based intrusion detection is able to identify known and unknown attacks. Many anomaly-based systems were designed using autoencoder techniques to reduce the high diminution of data and identify the intrusion in the resource-constrained IoT devices. This paper proposed an in-depth review of the approaches that used an autoencoder to detect attacks in an IoT environment. The main objective of this review is to find and address the current challenges of anomaly intrusion detection systems based on autoencoders as well as to present an overview of the different types of autoencoders that are used to enhance the effectiveness of intrusion detection systems. The findings of this paper show that autoencoders achieved an encouraging result in terms of reducing the dimensionality, improving the accuracy, and detecting anomalies. However, these approaches still suffer from high training time, complexity, and detection time. A robust adaptive anomaly intrusion detection system based on an autoencoder, to reduce the high dimensionality of data and detect attacks in IoT, is much needed.