Categorization of Anomalies in Smart Manufacturing Systems to Support the Selection of Detection Mechanisms
Categorization of Anomalies in Smart Manufacturing Systems to Support the Selection of Detection Mechanisms
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DOI：
10.1109/lra.2017.2714135
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发表时间：
2017-06
影响因子：
5.2
通讯作者：
Felipe Lopez;Miguel Saez;Yuru Shao;Efe C. Balta;J. Moyne;Z. Morley Mao;K. Barton;D. Tilbury
中科院分区：
文献类型：
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作者：
Felipe Lopez;Miguel Saez;Yuru Shao;Efe C. Balta;J. Moyne;Z. Morley Mao;K. Barton;D. Tilbury
智能制造系统中异常检测的一个重要问题是异常、故障和攻击的正式定义缺乏一致性。术语异常用于涵盖由不同类型的解决方案解决的各种情况。在这封信中，我们对机器、控制器和网络中的异常沿着检测机制进行了分类，并将它们统一在一个通用框架下，以帮助识别潜在的解决方案。建议的分类的主要贡献是，它允许识别智能制造系统中的异常检测的差距。
An important issue in anomaly detection in smart manufacturing systems is the lack of consistency in the formal definitions of anomalies, faults, and attacks. The term anomaly is used to cover a wide range of situations that are addressed by different types of solutions. In this letter, we categorize anomalies in machines, controllers, and networks along with their detection mechanisms, and unify them under a common framework to aid in the identification of potential solutions. The main contribution of the proposed categorization is that it allows the identification of gaps in anomaly detection in smart manufacturing systems.