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A Taxonomy of Supervised Learning for IDSs in SCADA Environments

机译：SCADA环境中IDSS监督学习的分类

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摘要

Supervisory Control and Data Acquisition (SCADA) systems play an important role in monitoring industrial processes such as electric power distribution, transport systems, water distribution, and wastewater collection systems. Such systems require a particular attention with regards to security aspects, as they deal with critical infrastructures that are crucial to organizations and countries. Protecting SCADA systems from intrusion is a very challenging task because they do not only inherit traditional IT security threats but they also include additional vulnerabilities related to field components (e.g., cyber-physical attacks). Many of the existing intrusion detection techniques rely on supervised learning that consists of algorithms that are first trained with reference inputs to learn specific information, and then tested on unseen inputs for classification purposes. This article surveys supervised learning from a specific security angle, namely SCADA-based intrusion detection. Based on a systematic review process, existing literature is categorized and evaluated according to SCADA-specific requirements. Additionally, this survey reports on well-known SCADA datasets and testbeds used with machine learning methods. Finally, we present key challenges and our recommendations for using specific supervised methods for SCADA systems.
机译：监督控制和数据采集（SCADA）系统在监测电力分配，运输系统，水分配和废水收集系统等工业过程中起重要作用。这些系统需要特别关注安全方面，因为它们处理对组织和国家至关重要的关键基础架构。从入侵保护SCADA系统是一个非常具有挑战性的任务，因为他们不仅继承传统的IT安全威胁，但它们还包括与现场组件（例如，网络物理攻击）其他漏洞。许多现有的入侵检测技术依赖于监督学习，该技术由首次使用参考输入训练的算法来学习特定信息，然后在寻找分类目的上进行测试。本文调查了从特定安全角度监督学习，即SCADA的入侵检测。基于系统审查过程，根据SCADA的要求，对现有文献进行分类和评估。此外，本调查报告了众所周知的SCADA数据集和与机器学习方法一起使用的试验台。最后，我们提出了关键挑战，并为使用SCADA系统的特定监督方法提供了关键挑战和我们的建议。

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