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基于遗传算法优化的OCSVM双轮廓模型异常检测算法
Alternative TitleAnomaly detection algorithm based on OCSVM double contour model of genetic algorithm optimization for industrial control system
闫腾飞1,2,3; 尚文利2,3,4; 赵剑明2,3,4; 乔枫1; 曾鹏2,3,4
Department工业控制网络与系统研究室
Source Publication计算机应用研究
ISSN1001-3695
2019
Volume36Issue:11Pages:1-6
Contribution Rank1
Funding Organization国家自然科学基金面上项目(61773368) ; 预研基金资助项目(6140242010116Zk63001)
Keyword工业控制系统 异常检测 遗传算法 单类支持向量机 双轮廓模态
Abstract针对Modbus工业总线协议的特殊性及工控数据样本的不均衡性,利用单类支持向量机(OCSVM)分别构建正常OCSVM模型和异常OCSVM模型,即双轮廓模态,模拟系统通信的正常模式和异常模式,实现工控系统异常检测。同时将遗传算法优化自变量降维应用于工控网络入侵检测场景,实现对输入自变量的降维压缩处理,防止OCSVM模型出现过拟合现象及分类准确率低的问题,提高异常检测的精度,缩减建模时间,并通过仿真验证了提出算法对工控网络异常检测的有效性。
Other AbstractThe Modbus industry bus protocol is special. And the network intrusion data sample of industrial control system is not balanced. So this paper used one-class support vector machine (OCSVM) to construct normal OCSVM model and abnormal OCSVM model to simulate the normal mode and abnormal mode of system communication. Then to realize the abnormal detection of industrial control system. In order to prevent the OCSVM model from overfitting and the low accuracy of classification, this paper used the genetic algorithm to the industrial control network by optimizing the dimensionality reduction of the independent variable. This method improves the accuracy of the anomaly detection and reduces the modeling time. Simulation results show that the proposed algorithm is effective for anomaly detection of industrial network.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22384
Collection工业控制网络与系统研究室
Corresponding Author尚文利
Affiliation1.沈阳建筑大学信息与控制工程学院
2.中国科学院沈阳自动化研究所
3.中科院网络化控制系统重点实验室
4.中国科学院大学
Recommended Citation
GB/T 7714
闫腾飞,尚文利,赵剑明,等. 基于遗传算法优化的OCSVM双轮廓模型异常检测算法[J]. 计算机应用研究,2019,36(11):1-6.
APA 闫腾飞,尚文利,赵剑明,乔枫,&曾鹏.(2019).基于遗传算法优化的OCSVM双轮廓模型异常检测算法.计算机应用研究,36(11),1-6.
MLA 闫腾飞,et al."基于遗传算法优化的OCSVM双轮廓模型异常检测算法".计算机应用研究 36.11(2019):1-6.
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