Application of BP Neural Network Model in Device State Detection of Industrial Control System

doi:10.13190/j.jbupt.2016.s.004

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2016, Vol. 39 ›› Issue (s1): 14-18.doi: 10.13190/j.jbupt.2016.s.004

• Papers • Previous Articles Next Articles

Application of BP Neural Network Model in Device State Detection of Industrial Control System

YAO Yun-zheng^1,2, YANG An¹, SHI Zhi-qiang¹, SUN Li-min¹

1. Key Laboratory of Networking Information Security Technology of the Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China;
2. School of Saftware, Beihang University, Beijing 100093, China

Received:2015-12-01 Online:2016-06-28 Published:2016-06-28

Abstract

Abstract:

The industrial control system (ICS) security is closely related to the security of national critical infrastructure, so, more and more countries began to increase the importance of ICS. Aiming at the physical devices in ICS field control net, an innovative intrusion detection algorithm was presented to analysis and estimate whether the devices are in normal operation condition. This algorithm is designed to detect internal or external intrusion actions in ICS and complex attack by maliciously using normative control commands.

Key words: industrial control system, device status, back propagation neural network, intrusion detection

CLC Number:

TN911.22

YAO Yun-zheng, YANG An, SHI Zhi-qiang, SUN Li-min. Application of BP Neural Network Model in Device State Detection of Industrial Control System[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(s1): 14-18.

[1]	YANG Lihua, NIE Qian, HU Bo, JIANG Ting. Channel Prediction Method Joint BP Neural Network with Basis Expansion Model [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(3): 13-18.
[2]	WANG Yi-fei, MO Shuang, WU Wen-rui, FAN Shao-hua, XIAO Ding. Internal-External Convolutional Networks for Network Intrusion Detection [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(5): 94-100.
[3]	LI Duan-ling, CHENG Li-wei, YU Gong-jing, ZHANG Zhong-hai, YU Shu-yue. An Electroencephalogram Signal Processing Method Fusing Wavelet Packet and Neural Network [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(3): 94-99.
[4]	QI Tian-tian, CHEN Yao, HE Cai-hou, LONG Sheng-rong, LI Qiu-feng. A Wavelet Packet Neural Network Feature Recognition Method for Damage Acoustic Emission Signals [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(1): 124-130.
[5]	DAI Jian-yong, DENG Xian-hong, WANG Bin, WANG Heng-hao. Clustering Routing Protocol for WSNs Based on Neural Network Optimization by Improved Firefly Algorithm [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 131-137.
[6]	JIANG Fang, ZHANG Nan-fei, HU Yan-jun, WANG Yi. BP Neural Network Based CSI Device-Free Target Classification Method [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(1): 40-45.
[7]	ZHAO Xiong-wen, SUN Ning-yao, GENG Sui-yan, ZHANG Yu, DU Fei. Time-Varying Channel Modeling Using Least Square Support Vector Machine [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(5): 29-35.
[8]	LI Min-zheng, LAN Jian-ping. Smart Home Intrusion Detection Algorithm Based on Spatial-Temporal Field Information Fusion [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(3): 76-84.
[9]	HU Yan-zhu, LI Lei-yuan. The Application of Kalman Filtering-BP Neural Network in Autonomous Positioning of End-Effector [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(6): 110-115.
[10]	ZHANG Ling, BAI Zhong-ying, XIE Kang. Dynamic Intrusion Detections with Vaccination [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(s1): 77-82.
[11]	QI Feng, LI Qi, HAN Qian, DU Yi, QIU Xue-song. A Neural Network Based Method to Assess Electric Power Communication Network Risk [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(1): 90-93.
[12]	ZHANG Lei;CHEN Jun-liang;MENG Xiang-wu;SHEN Xiao-yan;DUAN Kun. BP Neural Networks-Based Collaborative Filtering Recommendation Algorithm [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2009, 32(6): 42-46.
[13]	WU Bin, ZHENG Kang-feng, YANG Yi-xian . Analysis of Alert Correlation in Honeynet [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2008, 31(6): 63-66.
[14]	XIN Yang, WEI Jing-zhi, Xiu Xin-xin . A Fast Multiple Pattern Matching Using in Intrusion Detection [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2008, 31(3): 19-23.
[15]	. Intrusion Detection Technology Based on KFDA-SVM [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2007, 29(s1): 27-31.

Application of BP Neural Network Model in Device State Detection of Industrial Control System

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments