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Realistic Data Generation for Anomaly Detection in Industrial Settings Using Simulations

  • Peter SchneiderEmail author
  • Alexander Giehl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11387)

Abstract

With the rise of advanced persistent threats to cyber-physical facilities, new methods for anomaly detection are required. However, research on anomaly detection systems for industrial networks suffers from the lack of suitable training data to verify the methods at early stages. This paper presents a framework and workflow to generate meaningful training and test data for anomaly detection systems in industrial settings. Using process-model based simulations data can be generated on a large scale. We evaluate the data in regard to its usability for state-of-the-art anomaly detection systems. With adequate simulation configurations, it is even possible to simulate a sensor manipulation attack on the model and to derive labeled data.

By this simulation of attacked components, we demonstrate the effectiveness of systems trained on artificial data to detect previously unseen attacks.

Keywords

Anomaly detection Cyber-physical systems Modeling Security Simulation 

Notes

Acknowledgements

The presented work is part of the German national security reference project IUNO (http://www.iuno-projekt.de). The project is funded by the BMBF and aims to provide building-blocks for security in the emerging field of Industrie 4.0.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Fraunhofer AISECGarchingGermany

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