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Machine Learning use case in manufacturing – an evaluation of the model’s reliability from an IT security perspective

机译:制造中机器学习用例 - 从IT安全视角评估模型的可靠性

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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.
机译:如果运营商信任ML预测,则在制造环境中使用机器学习(ML)解决方案对于制造环境中的决策自动化的方法是至关重要的,而无需严重质疑它们。 ML-Applications对数据操纵,数据中毒和对抗性示例的脆弱性提高了对其可靠性和安全性的担忧。 本文通过IT安全透视评估了一个内边上预测维护解决方案,展示了模型的预测如何受到故意数据操作的影响,从而识别该特定用例的系统漏洞。 关于如何减轻威胁和管理风险的建议结束了。

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