Machine learning to automate network segregation for enhanced security in industry 4.0

Firooz B. Saghezchi*, Georgios Mantas, José Ribeiro, Alireza Esfahani, Hassan Alizadeh, Joaquim Bastos, Jonathan Rodriguez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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%.

Original languageEnglish
Title of host publicationBroadband Communications, Networks, and Systems - 9th International EAI Conference, Broadnets 2018, Proceedings
EditorsSaud Althunibat, Victor Sucasas, Georgios Mantas
PublisherSpringer
Pages149-158
Number of pages10
ISBN (Print)9783030051945
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event9th International EAI Conference on Broadband Communications, Networks, and Systems, Broadnets 2018 - Faro, Portugal
Duration: 19 Sep 201820 Sep 2018

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume263
ISSN (Print)1867-8211

Conference

Conference9th International EAI Conference on Broadband Communications, Networks, and Systems, Broadnets 2018
Country/TerritoryPortugal
CityFaro
Period19/09/1820/09/18

Keywords

  • Cyber-physical production systems
  • IIoT
  • Industry 4.0
  • Machine learning
  • MTC
  • Network segregation
  • Security
  • Traffic classification

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