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

  • Firooz B. SaghezchiEmail author
  • Georgios Mantas
  • José Ribeiro
  • Alireza Esfahani
  • Hassan Alizadeh
  • Joaquim Bastos
  • Jonathan Rodriguez
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 263)


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%.


Industry 4.0 Cyber-Physical Production Systems Security Machine learning Network segregation IIoT MTC Traffic classification 



The authors would like to thank Infineon Technologies, especially Christian Zechner and Stephan Spittaler for their great support in data acquisition and identifying the addressed challenges. It is also acknowledged that this work has been developed within Power Semiconductor and Electronics Manufacturing 4.0 (SemI40) project, under grant agreement No. 692466, co-funded by grants from Austria, Germany, Italy, France, Portugal (through Fundação para a Ciência e Tecnologia ECSEL/0009/2015) and Electronic Component Systems for European Leadership Joint Undertaking (ECSEL JU).


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Firooz B. Saghezchi
    • 1
    Email author
  • Georgios Mantas
    • 2
  • José Ribeiro
    • 2
  • Alireza Esfahani
    • 2
  • Hassan Alizadeh
    • 1
  • Joaquim Bastos
    • 2
  • Jonathan Rodriguez
    • 1
  1. 1.University of AveiroAveiroPortugal
  2. 2.Instituto de TelecomunicaçõesAveiroPortugal

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