| 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
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| Department | 工业控制网络与系统研究室 |
| Conference Name | 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 |
| Conference Date | July 31 - August 3, 2018 |
| Conference Place | New York |
| Author of Source | Columbia University ; IEEE ; IEEE Computer Society ; IEEE STC Smart Computing ; IEEE TCSC ; North America Chinese Talents Association |
| Source Publication | Proceedings - 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 |
| Publisher | IEEE |
| Publication Place | New York |
| 2018 | |
| Pages | 218-222 |
| Indexed By | EI ; CPCI（ISTP） |
| EI Accession number | 20184005895626 |
| WOS ID | WOS:000495072100032 |
| Contribution Rank | 1 |
| ISBN | 978-1-5386-4387-7 |
| Keyword | industrial control system Modbus communication protocol intrusion detection fuzzy C-means clustering supervised support vector (SVM) |
| Abstract | In 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 |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.sia.cn/handle/173321/23361 |
| Collection | 工业控制网络与系统研究室 |
| Corresponding Author | Shang WL(尚文利) |
| Affiliation | 1.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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Research on Industri（718KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Application Full Text | ||
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