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Machine Learning for Cyber Physical Systems pp 103–111Cite as

Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks

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Part of the Technologien für die intelligente Automation book series (TIA,volume 13)

Abstract

Condition monitoring systems based on deep neural networks are used for system failure detection in cyber-physical production systems. However, deep neural networks are vulnerable to attacks with adversarial examples. Adversarial examples are manipulated inputs, e.g. sensor signals, are able to mislead a deep neural network into misclassification. A consequence of such an attack may be the manipulation of the physical production process of a cyber-physical production system without being recognized by the condition monitoring system. This can result in a serious threat for production systems and employees. This work introduces an approach named CyberProtect to prevent misclassification caused by adversarial example attacks. The approach generates adversarial examples for retraining a deep neural network which results in a hardened variant of the deep neural network. The hardened deep neural network sustains a significant better classification rate (82% compared to 20%) while under attack with adversarial examples, as shown by empirical results.

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Authors and Affiliations

  1. Fraunhofer IOSB-INA, Lemgo, Germany

    Felix Specht & Jens Otto

Authors
  1. Felix Specht
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  2. Jens Otto
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Correspondence to Felix Specht .

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Editors and Affiliations

  1. Institut für Optronik, Systemtechnik und Bildauswertung, Fraunhofer, Karlsruhe, Germany

    Prof. Dr. Jürgen Beyerer

  2. MIT, Fraunhofer IOSB-INA, Lemgo, Germany

    Dr. Alexander Maier

  3. Helmut-Schmidt-Universität Hamburg, Hamburg, Germany

    Prof. Dr. Oliver Niggemann

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Specht, F., Otto, J. (2021). Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks. In: Beyerer, J., Maier, A., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 13. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62746-4_11

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  • DOI: https://doi.org/10.1007/978-3-662-62746-4_11

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