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Machine Learning to Automate Network Segregation for Enhanced Security in Industry 4.0

Machine Learning to Automate Network Segregation for Enhanced Security in Industry 4.0

Saghezchi, Firooz B., Mantas, Georgios, Ribeiro, José, Esfahani, Alireza, Alizadeh, Hassan, Bastos, Joaquim and Rodriguez, Jonathan (2018) Machine Learning to Automate Network Segregation for Enhanced Security in Industry 4.0. In: Broadband Communications, Networks, and Systems. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST) (263). Springer, Cham, Switzerland, pp. 149-158. ISBN 978-3030051945 ISSN 1867-8211 (doi:https://doi.org/10.1007/978-3-030-05195-2_15)

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Abstract

The heavy reliance of Industry 4.0 on emerging communication technologies, notably Industrial Internet-of-Things (IIoT) and Machine-Type Communications (MTC), and the increasing exposure of these traditionally isolated infrastructures to the Internet, are tremendously increasing the attack surface. Network segregation is a viable solution to address this problem. It essentially splits the network into several logical groups (subnetworks) and enforces adequate security policy on each segment, e.g., restricting unnecessary intergroup communications or controlling the access. However, existing segregation techniques primarily depend on manual configurations, which renders them inefficient for cyber-physical production systems because they are highly complex and heterogeneous environments with massive number of communicating machines. In this paper, we incorporate machine learning to automate network segregation, by efficiently classifying network end-devices into several groups through examining the traffic patterns that they generate. For performance evaluation, we analysed the data collected from a large segment of Infineon’s network in the context of the EU funded ECSEL-JU project “SemI40”. In particular, we applied feature selection and trained several supervised learning algorithms. Test results, using 10-fold cross validation, revealed that the algorithms generalise very well and achieve an accuracy up to 99.4%.

Item Type: Conference Proceedings
Title of Proceedings: Broadband Communications, Networks, and Systems
Additional Information: Proceedings of the 9th International EAI Conference, Broadnets 2018, Faro, Portugal, September 19–20, 2018.
Uncontrolled Keywords: Industry 4.0, Cyber-Physical Production Systems, Security, Machine Learning, Network Segregation, IIoT, MTC, Traffic Classification
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 18 Sep 2020 23:35
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/27716

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