Bretones Cassoli, Beatriz ; Ziegenbein, Amina ; Metternich, Joachim ; Đukanović, Siniša ; Hachenberger, Julien ; Laabs, Martin (2021)
Machine Learning Use Case in Manufacturing — An Evaluation of the Model's Reliability from an IT Security Perspective.
In: Procedia CIRP, 104
doi: 10.1016/j.procir.2021.11.195
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The use of Machine Learning (ML) solutions for decision automation in manufacturing environments is critical if operators trust ML-predictions without critically questioning them. The vulnerability of ML-applications to data manipulation, data-poisoning and adversarial examples raise concerns about its reliability and security. This paper evaluates an on-edge predictive maintenance solution through an IT security perspective, showing how the model's forecasting can be affected by intentional data manipulation and thus identifying the system's vulnerabilities for this particular use case. It concludes with suggestions on how to mitigate threats and manage risks.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Bretones Cassoli, Beatriz ; Ziegenbein, Amina ; Metternich, Joachim ; Đukanović, Siniša ; Hachenberger, Julien ; Laabs, Martin |
| Art des Eintrags: | Bibliographie |
| Titel: | Machine Learning Use Case in Manufacturing — An Evaluation of the Model's Reliability from an IT Security Perspective |
| Sprache: | Englisch |
| Publikationsjahr: | 26 November 2021 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 104 |
| DOI: | 10.1016/j.procir.2021.11.195 |
| Kurzbeschreibung (Abstract): | The use of Machine Learning (ML) solutions for decision automation in manufacturing environments is critical if operators trust ML-predictions without critically questioning them. The vulnerability of ML-applications to data manipulation, data-poisoning and adversarial examples raise concerns about its reliability and security. This paper evaluates an on-edge predictive maintenance solution through an IT security perspective, showing how the model's forecasting can be affected by intentional data manipulation and thus identifying the system's vulnerabilities for this particular use case. It concludes with suggestions on how to mitigate threats and manage risks. |
| Freie Schlagworte: | Artificial Intelligence, IT Security, Predictive Maintenance |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > Management industrieller Produktion |
| Hinterlegungsdatum: | 26 Jan 2022 07:06 |
| Letzte Änderung: | 28 Jan 2022 13:18 |
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