Zero-day attacks are one of the most dangerous threats to the security of modern systems, applications and infrastructure because they are unpredictable. Due to the unknown signatures of zero-day attacks, traditional signature-based defences are unable to detect them. Countering such attacks in IoT networks requires both in-depth research and the implementation of practical measures. The present review of state-of-the-art zero-day attack detection research has shown that deep learning approaches are best at detecting zero-day attacks and botnets in IoT networks. These approaches can analyse anomalies in network traffic and identify new threats and zero-day attacks while minimising the number of false positives.

Keywords: Zero-Day Attack, vulnerability, Internet of Things, machine learning, anomaly, signature-based defence method, autoencoder, network traffic