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首页> 外文期刊>IEEE Transactions on Systems, Man, and Cybernetics >Design and Analysis of Multimodel-Based Anomaly Intrusion Detection Systems in Industrial Process Automation
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Design and Analysis of Multimodel-Based Anomaly Intrusion Detection Systems in Industrial Process Automation

机译：工业过程自动化中基于多模型的异常入侵检测系统的设计与分析

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摘要

Industrial process automation is undergoing an increased use of information communication technologies due to high flexibility interoperability and easy administration. But it also induces new security risks to existing and future systems. Intrusion detection is a key technology for security protection. However, traditional intrusion detection systems for the IT domain are not entirely suitable for industrial process automation. In this paper, multiple models are constructed by comprehensively analyzing the multidomain knowledge of field control layers in industrial process automation, with consideration of two aspects: physics and information. And then, a novel multimodel-based anomaly intrusion detection system with embedded intelligence and resilient coordination for the field control system in industrial process automation is designed. In the system, an anomaly detection based on multimodel is proposed, and the corresponding intelligent detection algorithms are designed. Furthermore, to overcome the disadvantages of anomaly detection, a classifier based on an intelligent hidden Markov model, is designed to differentiate the actual attacks from faults. Finally, based on a combination simulation platform using optimized performance network engineering tool, the detection accuracy and the real-time performance of the proposed intrusion detection system are analyzed in detail. Experimental results clearly demonstrate that the proposed system has good performance in terms of high precision and good real-time capability.
机译：由于高度的灵活性，互操作性和易于管理，工业过程自动化正在越来越多地使用信息通信技术。但这也会给现有和未来的系统带来新的安全风险。入侵检测是安全保护的关键技术。但是，用于IT领域的传统入侵检测系统并不完全适合于工业过程自动化。本文通过综合分析工业过程自动化中现场控制层的多领域知识，并考虑物理和信息两个方面，构建了多个模型。然后，针对工业过程自动化领域的现场控制系统，设计了一种新型的基于多模型的具有嵌入式智能和弹性协调能力的异常入侵检测系统。该系统提出了一种基于多模型的异常检测方法，并设计了相应的智能检测算法。此外，为了克服异常检测的缺点，设计了基于智能隐马尔可夫模型的分类器，以区分实际攻击与故障。最后，在使用优化性能网络工程工具的组合仿真平台上，详细分析了所提出入侵检测系统的检测精度和实时性能。实验结果清楚地表明，所提出的系统在高精度和良好的实时能力方面具有良好的性能。

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