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Anomaly Detection in Industrial Networks: An Introduction

: Meshram, A.

Postprint urn:nbn:de:0011-n-4618262 (237 KByte PDF)
MD5 Fingerprint: 7b8a59a0efc1f24a7cb32d711dca2e41
Created on: 24.8.2017

Beyerer, Jürgen (Ed.); Pak, Alexey (Ed.):
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2016. Proceedings : Triberg-Nussbach, July, 24 to 29, 2016
Karlsruhe: KIT Scientific Publishing, 2017 (Karlsruher Schriften zur Anthropomatik 33)
ISBN: 978-3-7315-0678-2
DOI: 10.5445/KSP/1000070009
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2016, Triberg-Nussbach>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

With the advent of 21st Century, we stepped into the fourth industrial
revolution of cyber physical systems. The industrial components
are modular and capable of taking decentralized decisions in real time. The
processes can be virtualized and automated through inter-operable service
oriented components connected in a network. Therefore, there is need of
secured network systems and intrusion detection systems in order to detect
network attacks. Use of machine learning for anomaly detection in industrial
networks faces challenges which restricts its large-scale commercial deployment.
A roadmap is proposed to overcome the challenges. Real world network traffic for an industrial production is generated by IT Security Laboratory at Fraunhofer IOSB. The various attack vectors can be implemented under these circumstances and an adaptive hybrid analysis would reduce the errors of an intrusion detection system. Alarm correlation could be performed for semantic descriptions of detected results to network operator.