TY - GEN
T1 - Machine learning to automate network segregation for enhanced security in industry 4.0
AU - Saghezchi, Firooz B.
AU - Mantas, Georgios
AU - Ribeiro, José
AU - Esfahani, Alireza
AU - Alizadeh, Hassan
AU - Bastos, Joaquim
AU - Rodriguez, Jonathan
N1 - Funding Information:
Acknowledgment. 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).
Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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%.
AB - 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%.
KW - Cyber-physical production systems
KW - IIoT
KW - Industry 4.0
KW - Machine learning
KW - MTC
KW - Network segregation
KW - Security
KW - Traffic classification
U2 - 10.1007/978-3-030-05195-2_15
DO - 10.1007/978-3-030-05195-2_15
M3 - Conference contribution
AN - SCOPUS:85059783006
SN - 9783030051945
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 149
EP - 158
BT - Broadband Communications, Networks, and Systems - 9th International EAI Conference, Broadnets 2018, Proceedings
A2 - Althunibat, Saud
A2 - Sucasas, Victor
A2 - Mantas, Georgios
PB - Springer
T2 - 9th International EAI Conference on Broadband Communications, Networks, and Systems, Broadnets 2018
Y2 - 19 September 2018 through 20 September 2018
ER -