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      Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security

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          Abstract

          The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

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          Most cited references 72

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          Internet of Things in Industries: A Survey

           Li Xu,  Wu-bin He,  Shancang Li (2014)
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            Software-Defined Networking: A Comprehensive Survey

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              A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                15 July 2019
                July 2019
                : 19
                : 14
                Affiliations
                [1 ]Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11451, Saudi Arabia
                [2 ]Department of Electronics and Computer Science, USTHB University, Bab Ezzouar 16111, Algeria
                [3 ]Department of Computer Science, King Saud University, Riyadh 11451, Saudi Arabia
                [4 ]School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK
                [5 ]Department of Computer Science, Guelma University, Guelma 24000, Algeria
                [6 ]Guangdong Provincial Key Lab of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China
                Author notes
                [* ]Correspondence: abderhab@ 123456ksu.edu.sa ; Tel.: +966-11-469-7350
                Article
                sensors-19-03119
                10.3390/s19143119
                6679272
                31311136
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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