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Generation of adversarial examples to prevent misclassification of deep neural network based condition monitoring systems for cyber-physical production systems

 
: Specht, Felix; Otto, Jens; Niggemann, Oliver; Hammer, Barbara

:
Postprint urn:nbn:de:0011-n-5256162 (1.1 MByte PDF)
MD5 Fingerprint: 73201b604e05d1833d306a9ce97e3397
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Erstellt am: 8.1.2019


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 16th International Conference on Industrial Informatics, INDIN 2018. Proceedings : 18-20 July 2018, Porto, Portugal
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-4829-2
ISBN: 978-1-5386-4828-5
ISBN: 978-1-5386-4830-8
S.760-765
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Deep neural network based condition monitoring systems are used to detect system failures of cyber-physical production systems. However, a vulnerability of deep neural networks are adversarial examples. They are manipulated inputs, e.g. process data, with the ability to mislead a deep neural network into misclassification. Adversarial example attacks can manipulate the physical production process of a cyber-physical production system without being recognized by the condition monitoring system. Manipulation of the physical process poses a serious threat for production systems and employees. This paper introduces CyberProtect, a novel approach to prevent misclassification caused by adversarial example attacks. CyberProtect generates adversarial examples and uses them to retrain deep neural networks. This results in a hardened deep neural network with a significant reduced misclassification rate. The proposed countermeasure increases the classification rate from 20% to 82%, as proved by empirical results.

: http://publica.fraunhofer.de/dokumente/N-525616.html