Open Access

ARTICLE

An Intelligent Hybrid Mutual Authentication Scheme for Industrial Internet of Thing Networks

Muhammad Adil¹, Jehad Ali², Muhammad Sajjad Khan³, Junsu Kim³, Ryan Alturki⁴, Mohammad Zakarya⁴, Mukhtaj Khan⁴, Rahim Khan⁴, Su Min Kim^3,*

1 Department of Computer Science, Virtual University of Pakistan, Lahore, 54000, Pakistan
2 Department of Computer Engineering, Department of AI Convergence Network, Ajou University, Suwon, 16499, South Korea
3 Department of Electronics Engineering, Korea Polytechnic University, Slheung, South Korea
4 Department of Computer Science, Abdul Wali Khan University Mardan, 23200, Pakistan

* Corresponding Author: Su Min Kim. Email:

(This article belongs to this Special Issue: Security Issues in Industrial Internet of Things)

Computers, Materials & Continua 2021, 68(1), 447-470. https://doi.org/10.32604/cmc.2021.014967

Received 29 October 2020; Accepted 30 November 2020; Issue published 22 March 2021

Abstract

Internet of Things (IoT) network used for industrial management is vulnerable to different security threats due to its unstructured deployment, and dynamic communication behavior. In literature various mechanisms addressed the security issue of Industrial IoT networks, but proper maintenance of the performance reliability is among the common challenges. In this paper, we proposed an intelligent mutual authentication scheme leveraging authentication aware node (AAN) and base station (BS) to identify routing attacks in Industrial IoT networks. The AAN and BS uses the communication parameter such as a route request (RREQ), node-ID, received signal strength (RSS), and round-trip time (RTT) information to identify malicious devices and routes in the deployed network. The feasibility of the proposed model is validated in the simulation environment, where OMNeT++ was used as a simulation tool. We compare the results of the proposed model with existing field-proven schemes in terms of routing attacks detection, communication cost, latency, computational cost, and throughput. The results show that our proposed scheme surpasses the previous schemes regarding these performance parameters with the attack detection rate of 97.7 %.

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Keywords

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