Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning
Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning
复制标题
不平衡数据集对使用机器学习的工业物联网安全的影响
DOI：
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复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
R. Jain
中科院分区：
文献类型：
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作者：
Maede Zolanvari;M. Teixeira;R. Jain
机器学习算法已被证明适用于IT系统的安全平台。然而，由于工业物联网（IIoT）与常规IT网络之间的根本差异，需要考虑特殊的绩效评估。工业物联网系统的漏洞和安全需求需要不同的考虑。在本文中，我们研究了为什么机器学习必须集成到工业物联网的安全机制中，以及它目前在哪些方面缺乏令人满意的性能。在我们的实验设计中研究了与此问题相关的挑战和现实世界的考虑。我们使用类似于真实工业工厂的IIoT测试平台来展示我们的概念验证。
Machine learning algorithms have been shown to be suitable for securing platforms for IT systems. However, due to the fundamental differences between the industrial internet of things (IIoT) and regular IT networks, a special performance review needs to be considered. The vulnerabilities and security requirements of IIoT systems demand different considerations. In this paper, we study the reasons why machine learning must be integrated into the security mechanisms of the IIoT, and where it currently falls short in having a satisfactory performance. The challenges and real-world considerations associated with this matter are studied in our experimental design. We use an IIoT testbed resembling a real industrial plant to show our proof of concept.