Abstract

The Internet of Things (IoT) aims to provide connectivity between all computing entities. However, this facilitates cyberthreats, which exploits the existence of vulnerability over a period. The zero-day threat is one of the vulnerabilities that can result in zero-day attacks that are destructive to the network security and an enterprise. This attack may have potentially compromised critical infrastructure, far-reaching consequences, national security, and even personal privacy. To alleviate the risks, organizations and manufacturers should prioritize proactive security measures, involving robust authentication mechanisms, ongoing monitoring, and timely software updates, to defend the IoT ecosystem from emerging threats. In present scenario, deep learning (DL)-based models have improved robustness in learning data giving it an improved capability to identify unknown information, since it can able to extract knowledge of non-linear data to identify unknown information. The study presents a Robust Zero-Day Attack Detection with Optimal Deep Learning (RZDAD-ODL) technique for the IoT framework. The primary intention of the RZDAD-ODL model lies in the automatic and effectual detection of zero-day attacks in the IoT framework. In the presented RZDAD-ODL technique, the honey badger algorithm (HBA) can be used for the optimum range of the features. Besides, the RZDAD-ODL technique exploits the conditional variational autoencoder (CVAE) model for attack detection and its parameter tuning process can be performed by using a rider optimization algorithm (ROA). The experimentation results of the RZDAD-ODL system can be validated on a benchmark dataset. Extensive comparison studies reported the better attack detection performance of the RZDAD-ODL model over other current techniques.