Open Access

ARTICLE

Artificial Intelligence Based Threat Detection in Industrial Internet of Things Environment

Fahad F. Alruwaili^*

College of Computing and Information Technology, Shaqra University, Sharqa, Saudi Arabia

* Corresponding Author: Fahad F. Alruwaili. Email:

Computers, Materials & Continua 2022, 73(3), 5809-5824. https://doi.org/10.32604/cmc.2022.031613

Received 22 April 2022; Accepted 07 June 2022; Issue published 28 July 2022

Abstract

Internet of Things (IoT) is one of the hottest research topics in recent years, thanks to its dynamic working mechanism that integrates physical and digital world into a single system. IoT technology, applied in industries, is termed as Industrial IoT (IIoT). IIoT has been found to be highly susceptible to attacks from adversaries, based on the difficulties observed in IIoT and its increased dependency upon internet and communication network. Intentional or accidental attacks on these approaches result in catastrophic effects like power outage, denial of vital health services, disruption to civil service, etc., Thus, there is a need exists to develop a vibrant and powerful for identification and mitigation of security vulnerabilities in IIoT. In this view, the current study develops an AI-based Threat Detection and Classification model for IIoT, abbreviated as AITDC-IIoT model. The presented AITDC-IIoT model initially pre-processes the input data to transform it into a compatible format. In addition, Whale Optimization Algorithm based Feature Selection (WOA-FS) is used to elect the subset of features. Moreover, Cockroach Swarm Optimization (CSO) is employed with Random Vector Functional Link network (RVFL) technique for threat classification. Finally, CSO algorithm is applied to appropriately adjust the parameters related to RVFL model. The performance of the proposed AITDC-IIoT model was validated under benchmark datasets. The experimental results established the supremacy of the proposed AITDC-IIoT model over recent approaches.

---

Keywords

---