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题名: Intrusion detection algorithm based on OCSVM in industrial control system
作者: Shang WL(尚文利); Zeng P(曾鹏); Wan M(万明); Li L(李琳); An PF(安攀峰)
作者部门: 工业控制网络与系统研究室
关键词: SVM ; intrusion detection ; PSO
刊名: Security and Communication Networks
ISSN号: 1939-0114
出版日期: 2016
卷号: 9, 期号:10, 页码:1014-1049
收录类别: SCI ; EI
产权排序: 1
项目资助者: National Natural Science Foundation of China [61501447] ; Independent project of Key Laboratory of Networked Control System Chinese Academy of Sciences: Research on abnormal behavior modeling, online intrusion detection and self-learning method in industrial control network
摘要: In order to detect abnormal communication behaviors efficiently in today's industrial control system, a new intrusion detection algorithm based on One-Class Support Vector Machine (OCSVM) is proposed in this paper. In this algorithm, a normal communication behavior model is established by using OCSVM, and the Particle Swarm Optimization algorithm is designed to optimize OCSVM model parameters. Furthermore, we adopt the normal Modbus function code sequence to train OCSVM model, and then use this model to detect abnormal Modbus TCP traffic. Our simulation results show that the proposed algorithm not only is efficient and reliable but also meets the real-time requirements of anomaly detection in industrial control system.
语种: 英语
WOS记录号: WOS:000379052200009
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/17603
Appears in Collections:工业控制网络与系统研究室_期刊论文

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Recommended Citation:
Shang WL,Zeng P,Wan M,et al. Intrusion detection algorithm based on OCSVM in industrial control system[J]. Security and Communication Networks,2016,9(10):1014-1049.
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