基于扩展Kalman滤波的工业控制系统DDoS攻击检测

发布时间：2018-03-16 09:04

本文选题：扩展卡尔曼滤波　切入点：模型参数识别　出处：《电子技术应用》2016年04期 　论文类型：期刊论文

【摘要】：随着工业化和信息化的不断融合,越来越多的工业控制系统接入互联网或企业管理网。不同于传统的信息系统,工业控制系统对于实时性的要求非常高,相同的控制策略在不同的采样频率下,系统的动态响应会有较大的差异。当工业控制系统遭受到分布式拒绝服务(DDoS)的攻击时,传统的攻击检测方法虽然可以较好地完成检测任务,但会给工业控制网络造成较大的负担,从而影响整个系统的稳定性和实时性。结合工业控制系统的网络结构、网络特性以及流量平稳等特点,提出一种扩展Kalman滤波和控制系统的模型参数识别的算法来检测DDo S的攻击。在搭建的SCADA工业控制仿真系统平台中进行测试和实验,结果表明,所提出的算法可以有效地识别出针对特定工业控制系统网络的DDo S攻击。
[Abstract]:With the continuous integration of industrialization and information, more and more industrial control systems are connected to the Internet or enterprise management networks. Different from traditional information systems, industrial control systems require very high real-time performance. The dynamic response of the system will vary greatly with the same control strategy at different sampling frequencies. When the industrial control system is attacked by the distributed denial of Service (DDoS), Although the traditional attack detection method can accomplish the detection task well, it will cause a great burden on the industrial control network, thus affecting the stability and real-time performance of the whole system, combining with the network structure of the industrial control system, This paper presents an extended Kalman filter and model parameter identification algorithm to detect the attack of DDo S in the SCADA industrial control simulation system platform. The proposed algorithm can effectively identify the DDo S attacks against specific industrial control system networks.
【作者单位】：工业和信息化部电子科学技术情报研究所;
【基金】：工信部工业转型升级重点项目(ZB1514003);工信部财务司专项(ZB1314002);工信部购买服务专项(SX1514074)
【分类号】：TP393.08

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