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国外文献 > 3.企业技术文献 > TIMKEN铁姆肯 >

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标 题： Analysis of Machine Learning Algorithms for Cyber Attack Detection in SCADA Power Systems
SCADA电力系统中机器学习算法用于网络攻击检测的分析
作 者： Mitchell Timken
Onat Gungor
Tajana Rosing
Baris Aksanli
关键词：
出版年： 2023
文集名： 2023 International Conference on Smart Applications, Communications and Networking (SmartNets 2023)
2023年智能应用、通信与网络国际会议（SmartNets 2023）
会议名： 2023 International Conference on Smart Applications, Communications and Networking (SmartNets 2023)
2023年智能应用、通信与网络国际会议（SmartNets 2023）
届 次：
摘 要： Cybersecurity is a rapidly growing concern in many technological areas worldwide. Supervisory Control and Data Acquisition (SCADA) systems are especially vulnerable to cyber attacks due to increased inter-connectivity. SCADA systems need to be equipped with the proper tools and techniques to detect cyber attacks, distinguish them accurately from normal traffic, overcome cyber attacks when present, and prevent future cyber attacks from disrupting these systems. In this paper, we first analyze 10 well-known traditional machine learning algorithms in terms of how effective they are when detecting cyber attacks. Then, we construct a stacking ensemble learner using these methods via different meta learners. Our experiments show that ensemble methods perform better than individual methods, demonstrating the need for a more comprehensive solution when defending against cyber attacks in SCADA systems.

网络安全已成为全球众多技术领域日益关注的问题。由于互联性不断增强，监控控制与数据采集（SCADA）系统尤其容易遭受网络攻击。SCADA系统需要配备适当的工具和方法，以检测网络攻击、准确区分其与正常流量、在遭受攻击时有效应对，并防止未来网络攻击干扰这些系统。本文首先分析了10种常见的传统机器学习算法在检测网络攻击方面的有效性，然后通过不同的元学习器构建了一个堆叠集成学习器。实验结果表明，集成方法优于单一方法，这凸显了在防御SCADA系统中的网络攻击时，需要更全面的解决方案。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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