多元统计过程控制在过程控制系统中区分干扰与入侵的可行性研究

Mikel Iturbe, J. Camacho, Iñaki Garitano, Urko Zurutuza, Roberto Uribeetxeberria
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引用次数: 13

摘要

过程控制系统(pcs)是关键基础设施(ci)的运行核心。因此,异常检测一直是保证CI正常运行的一个活跃研究领域。以前的方法利用网络级数据进行异常检测,或者忽略了进程干扰的存在,从而打开了将干扰错误标记为攻击的可能性,反之亦然。本文提出了一种基于多元统计过程控制(MSPC)的异常检测和诊断系统,旨在区分攻击和干扰。为此,我们将传统的MSPC扩展到监控过程级和控制器级数据。我们使用田纳西-伊士曼流程来评估我们的方法。结果表明,我们的方法可以在一定程度上用于区分干扰和入侵,并且我们得出结论,我们提出的方法可以扩展到其他数据源以改善结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems Using Multivariate Statistical Process Control
Process Control Systems (PCSs) are the operating core of Critical Infrastructures (CIs). As such, anomaly detection has been an active research field to ensure CI normal operation. Previous approaches have leveraged network level data for anomaly detection, or have disregarded the existence of process disturbances, thus opening the possibility of mislabelling disturbances as attacks and vice versa. In this paper we present an anomaly detection and diagnostic system based on Multivariate Statistical Process Control (MSPC), that aims to distinguish between attacks and disturbances. For this end, we expand traditional MSPC to monitor process level and controller level data. We evaluate our approach using the Tennessee-Eastman process. Results show that our approach can be used to distinguish disturbances from intrusions to a certain extent and we conclude that the proposed approach can be extended with other sources of data for improving results.
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