A Novel Classifier for Cyber Attack Detection System in Industrial Internet of Things

Abstract

The usage of the Internet of Things (IoT) conception in the industrial sector along with applications is referred to as the Industrial Internet of Things (IIoT). Various applications have been subsumed in the IIoT. Nevertheless, cybercriminals mostly target these systems. Thus, here, a novel methodology of Cyber Attack Detection (CAD) system has been proposed in IIoT to overcome the aforementioned issue. UNSW-NB2015 and DS2OS are the two IIoT datasets utilized in this work. Initially, in both datasets, the missing values are replaced; subsequently, the feature extraction is performed. Next, by utilizing Poisson Distribution-based Naked Mole Rat Optimization Algorithm (PD-NMROA), the significant features are selected as of both datasets. After that, by employing MaHalanobis distance-based K-Means (MaH-KMeans) algorithm, the features extracted as of the datasets are normalized along with clustered. Eventually, to classify the data, the clustered features are inputted to the TanSwish - Restricted Boltzmann Dense Machines (TS-RBDMs). The experiential outcomes displayed that the proposed methodology obtained higher efficacy in contrast to the prevailing systems.