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基于优化单类支持向量机的工业控制系统入侵检测算法
Alternative TitleIntrusion Detection Algorithm Based on Optimized One-class Support Vector Machine for Industrial Control System
尚文利; 李琳; 万明; 曾鹏
Department工业控制网络与系统研究室
Source Publication信息与控制
ISSN1002-0411
2015
Volume44Issue:6Pages:678-684
Indexed ByCSCD
CSCD IDCSCD:5630309
Contribution Rank1
Funding Organization国家自然科学基金资助项目(61501447) ; 中国科学院网络化控制系统重点实验室自主课题资助项目
Keyword单类支持向量机 入侵检测 Modbus功能码 粒子群优化
Abstract基于通信行为的异常检测是工业控制系统入侵检测的难点问题. 通过利用粒子群优化(particle swarm optimization,PSO)算法对单类支持向量机(one-class support vector machine,OCSVM)算法的参数进行优化,提出一种PSO-OCSVM算法. 该算法根据正常的Modbus功能码序列建立正常通信行为的入侵检测模型,识别出异常的Modbus TCP通信流量. 通过仿真对比分析,证明PSO-OCSVM算法满足工业控制系统通信异常检测对高效性、可靠性和实时性的需求.
Other AbstractThe detection of anomalous communication behavior is a challenging problem with respect to detecting intrusions in industrial control systems. We utilize the particle swarm optimization (PSO) algorithm to optimize the parameters of the one-class support vector machine (OCSVM), and further propose the PSO-OCSVM algorithm. According to the function codes of the standard Modbus transmission control protocol (TCP), we developed an intrusion detection model of normal communication behavior to enable the identification of abnormal Modbus TCP communication. A comparison and analysis of the simulation confirms that the proposed algorithm is demonstrably efficient, reliable, and operates in real-time, and thus has the potential to meet the requirements of anomaly detection in industrial control systems.
Language中文
Citation statistics
Cited Times:7[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/17541
Collection工业控制网络与系统研究室
Corresponding Author尚文利
Affiliation1.中国科学院沈阳自动化研究所
2.沈阳理工大学信息科学与工程学院
Recommended Citation
GB/T 7714
尚文利,李琳,万明,等. 基于优化单类支持向量机的工业控制系统入侵检测算法[J]. 信息与控制,2015,44(6):678-684.
APA 尚文利,李琳,万明,&曾鹏.(2015).基于优化单类支持向量机的工业控制系统入侵检测算法.信息与控制,44(6),678-684.
MLA 尚文利,et al."基于优化单类支持向量机的工业控制系统入侵检测算法".信息与控制 44.6(2015):678-684.
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