BRL-ETDM: Bayesian reinforcement learning-based explainable threat detection model for industry 5.0 network.

To enhance the universal adaptability of the Real-Time deployment of Industry 5.0, various machine learning-based cyber threat detection models are given in the literature. Most of the existing threat detection models may not be able to detect zero-day cyber threats and are prone to producing a high False Positive Rate (FPR) due to irrelevant features and imbalanced class samples. Furthermore, its predictive decisions are also difficult to comprehend even by security experts. Consequently, an intelligent and more robust model is needed to mitigate zero-day cyber threats. This study proposes an explainable model named BRL-ETDM for detecting cyber threats in Industry 5.0. In this model, features are optimized by Bayesian Reinforcement Learning (BRL)-based Bee Swarm Optimization (BSO) technique in which the exploitation phase of BSO is improved by the BRL technique. Then, an improved weighted majority voting-based ensemble technique is designed to enhance threat detection performance. Additionally, an explainable AI technique is employed to explain the threat predictions. This model is tested and validated using two realistic datasets named Edge-IIoTset and ToN-IoT. Experimental results show that the proposed model achieved a maximum accuracy of 96.15% with a minimum number of features and FPR of 0.27% as compared to existing techniques. [ABSTRACT FROM AUTHOR]