CL2ES-KDBC: A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things (IoT) is a growing technology that allows the sharing of data with other devices across wireless networks. Specifically, IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks. In this framework, a Covariance Linear Learning Embedding Selection (CL2ES) methodology is used at first to extract the features highly associated with the IoT intrusions. Then, the Kernel Distributed Bayes Classifier (KDBC) is created to forecast attacks based on the probability distribution value precisely. In addition, a unique Mongolian Gazellas Optimization (MGO) algorithm is used to optimize the weight value for the learning of the classifier. The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyberattack datasets, The obtained results are then compared with current classification methods regarding accuracy (97%), precision (96.5%), and other factors. Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed, which provides valuable insight into its performance, efficiency, and suitability for securing