SIA OpenIR  > 工业控制网络与系统研究室
Research on Industrial Control Anomaly Detection Based on FCM and SVM
Shang WL(尚文利)1,2; Cui JR(崔君荣)1,3; Song CH(宋纯贺)1,2; Zhao JM(赵剑明)1,2; Zeng P(曾鹏)1,2
Department工业控制网络与系统研究室
Conference Name17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
Conference DateJuly 31 - August 3, 2018
Conference PlaceNew York
Author of SourceColumbia University ; IEEE ; IEEE Computer Society ; IEEE STC Smart Computing ; IEEE TCSC ; North America Chinese Talents Association
Source PublicationProceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
PublisherIEEE
Publication PlaceNew York
2018
Pages218-222
Indexed ByEI
EI Accession number20184005895626
Contribution Rank1
ISBN978-1-5386-4387-7
Keywordindustrial control system Modbus communication protocol intrusion detection fuzzy C-means clustering supervised support vector (SVM)
AbstractIn order to solve the problem of virus and Trojan attacking the application layer network protocol of industrial control system, the rule of Modbus/TCP communication protocol is analyzed. An intrusion detection method based on clustering and support vector machine is proposed. The method combines unsupervised fuzzy C-means clustering (FCM) with supervised support vector (SVM) machine to calculate the distance between industrial control network communication data and cluster center. Partial data satisfying the threshold condition is further classified by support vector machine. Experimental results show that compared with the traditional intrusion detection method, this method can effectively reduce the training time and improve the classification accuracy without needing to know the class label in advance.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23361
Collection工业控制网络与系统研究室
Corresponding AuthorShang WL(尚文利)
Affiliation1.Shenyang Institute of Automation, Chinese, Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
3.School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, China
Recommended Citation
GB/T 7714
Shang WL,Cui JR,Song CH,et al. Research on Industrial Control Anomaly Detection Based on FCM and SVM[C]//Columbia University, IEEE, IEEE Computer Society, IEEE STC Smart Computing, IEEE TCSC, North America Chinese Talents Association. New York:IEEE,2018:218-222.
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