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Anomaly detection in industrial networks using machine learning: A roadmap

 
: Meshram, A.; Haas, Christian

:

Beyerer, Jürgen (Ed.); Niggemann, Oliver (Ed.); Kühnert, Christian (Ed.):
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2016, held at the Fraunhofer IOSB in Karlsruhe, September 29th, 2016
Berlin: Springer Vieweg, 2017 (Technologien für die intelligente Automation 3)
ISBN: 978-3-662-53805-0 (Print)
ISBN: 978-3-662-53806-7
S.65-72
Conference on Machine Learning for Cyber-Physical-Systems and Industry 4.0 (ML4CPS) <2, 2016, Karlsruhe>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()
machine learning; Industrial Network Security; anomaly detection

Abstract
With the advent of 21st Century, we stepped into the fourth industrial revolution of cyber physical systems. There is the 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. ADIN Suite proposes a roadmap to overcome these challenges with multi-module solution. It solves the need for real world network traffic, an adaptive hybrid analysis to reduce error rates in diverse network traffic and alarm correlation for semantic description of detection results to the network operator.

: http://publica.fraunhofer.de/dokumente/N-432112.html