Innovative IoT Threat Detection: Weighted Variational Autoencoder-Based Hunter Prey Search Algorithm for Strengthening Cybersecurity

Abstract

Cybersecurity threat detection in the Internet of Things (IoT) identifies and mitigates risks associated with connected devices. The IoT devices are vulnerable to attacks as they do not contain any robust security mechanism. Traditional methods for cybersecurity-based threat detection face significant problems such as scalability, privacy concerns, and resource constraints when applied to the dynamic IoT environment. To tackle these challenges, this paper proposed a novel Weighted Variational Autoencoder-based Hunter Prey Search (WVA-HPS) algorithm for enhancing cybersecurity threat detection in IoT. In this study, a weighted Variational Autoencoder is employed for regulating weight mechanism using weight regularization and the weight average ensemble method. The Hunter Prey Search optimization (HPSO) algorithm is utilized for minimizing overfitting issues to enhance the efficiency of the WVA method. The proposed WVA-HPS model comprises five different stages such as data collection, data preprocessing, threat detection, model evaluation, and output. The study is validated on diverse datasets namely BoT-IoT, MQTTset, and IoT-23. The WVA-HPS method's performance is analyzed using the metrics namely precision, accuracy, specificity, F-measure, and recall and its performance is compared with existing methods. The experimental results illustrate the performance of the WVA-HPS method for cybersecurity threat detection in an IoT environment.