Modeling of Bayesian Machine Learning with Sparrow Search Algorithm for Cyberattack Detection in IIoT Environment

With the fast-growing interconnection of smart technologies, the Industrial Internet of Things (IIoT) has revolutionized how the industries work by connecting devices and sensors through automating regular operations via the Internet of Things (IoTs). IoT device provides seamless diversity and connectivity in different application domains. This system and its transmission channels are subjected to targeted cyberattacks due to their round-the-clock connectivity. Accordingly, a multilevel security solution is needed to safeguard the industrial system. By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. This study introduces a new Bayesian Machine Learning with the Sparrow Search Algorithm for Cyberattack Detection (BMLSSA-CAD) method in the IIoT networks. The proposed BMLSSA-CAD technique mainly intends to improve security in the IIoT network via the detection of cyberattacks. In the BMLSSA-CAD technique, the min-max scalar can be used to normalize the input dataset. Besides, the BMLSSA-CAD method involves a chameleon optimization algorithm (COA) based feature selection approach to elect an optimum feature set. The BMLSSA-CAD technique uses the Bayesian Belief Networks (BBN) model for cyberattack detection. The parameter tuning process was executed by using the sparrow search algorithm (SSA) to boost the BBN model performance. The performance of the BMLSSA-CAD algorithm can be studied using a benchmark dataset. The simulation outcomes highlighted that the BMLSSA-CAD method accomplishes improved security in the IIoT platform.