[1]XIONG Liu,CAO Xiedong,LI Jie,et al.Study and simulation of the SCADA security factors neuron’scloud inference engine[J].CAAI Transactions on Intelligent Systems,2016,11(5):688-695.[doi:10.11992/tis.201509020]
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Study and simulation of the SCADA security factors neuron’scloud inference engine

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 5 Page number: 688-695 Column: 学术论文—脑认知基础 Public date: 2016-11-01
Title:
Study and simulation of the SCADA security factors neuron’scloud inference engine
Author(s):
XIONG Liu1;  CAO Xiedong1;  LI Jie1;  YANG Li2;  LIU Zengliang3
1. School of Electrical Information Engineering, Southwest Petroleum University, Chengdu 610500, China;
2. School of Computer Science, Southwest Petroleum University, Chengdu 610500, China;
3. Institute of Information Operation, University of National Defense, Beijing 100091, China
Keywords:
SCADA information security; factor space; factor neuron; cloud model; MATLAB simulation
CLC:
TP18
DOI:
10.11992/tis.201509020
Abstract:
Over recent years, with the amount of incidents involving industrial control information systems, the safety of these system has been given increased importance across the Globe, and several technical measures have been implented to improve this. This paper proposed an active defense method based on a factor neural network in reference to SCADA information security. The different aspects of SCADA information security were mapped to factor coordinates, and the factor neuron method for knowledge factors was then used to transform this from a fuzzy concept (represented by language) to quantitative data through a cloud inference engine. Inference was then conducted through generated multi conditions and multi rules based on a cloud model, so that it could then be transformed into a qualitative language (e.g.‘more likely’based on Ex) to be able to forecast the consequences of unknown malicious programs. This paper focus on using a generator with multiple conditions and rules based on a cloud model to achieve inference, thus providing an idea of the oil and gas SCADA information security’s defense response using algorithmic design and MATLAB-based simulations.
References:
[1] 刘增良. 因素神经网络理论及其应用[M]. 贵阳:贵州科技出版社, 1994. LIU Zengliang. Factor neural networks and its application[M]. Guiyang:Guizhou Science and Technology Publishing House, 1994.
[2] YANG Li, GAO Xiedong, LI Jie, et al. Research on FNN-based security defence architecture model of scada network[C]//Proceedings of the 2nd International Conference on Cloud Computing and Intelligence Systems. Hangzhou, China, 2012.
[3] QIN Yong, CAO Xiedong, LIANG Peng, et al. Research on the analytic factor neuron model based on cloud generator and its application in oil & gas scada security defense[C]//Proceedings of 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems. Shenzhen, China, 2014:5.
[4] 刘增良. 知识的因素表示[C]//中国科学技术协会首届青年学术年会论文集(工科分册·上册). 北京, 1992:5. LIU Zengliang. The factor representation of knowledge[C]//Proceedings of the 1st National Association of Science and Technology of Youth (Engineering. First). Beijing, 1992:5.
[5] 杨朝晖, 李德毅. 二维云模型及其在预测中的应用[J]. 计算机学报, 1998, 21(11):961-969. YANG Zhaohui, LI Deyi. Planar model and its application in prediction[J]. Chinese journal of computers, 1998, 21(11):961-969.
[6] 汪培庄. 因素空间与概念描述[J]. 软件学报, 1992, 3(2):30-40. WANG Peizhuang. Factor space and description of concepts[J]. Journal of software, 1992, 3(2):30-40.
[7] 王树良. 基于数据场与云模型的空间数据挖掘和知识发现[D]. 武汉:武汉大学, 2002. WANG Shuliang. Data field and cloud model based spatial data mining and knowledge discovery[D]. Wuhan:Wuhan University, 2002.
[8] 曹谢东. 模糊信息处理及应用[M]. 北京:科学出版社, 2003:103-104. CAO Xiedong. Fuzzy information processing and application[M]. Beijing:Science Press, 2003:103-104.
[9] YANG Li, CAO Xiedong, LI Jie, et al. A new formal description model of network attacking and defence knowledge of oil and gas field SCADA system[M]//WANG Hua, ZOU Lei, HUANG Guangyan, et al. Web Technologies and Applications. Berlin Heidelberg:Springer, 2012.
[10] YANG Li. A new factor state space model for SCADA network attack and defense[J]. International journal of security and its applications, 2014, 8(6):303-314.
[11] 王盼卿, 李晓辉. 一种基于知识因素表示理论的装备信息分类描述方法[J]. 国防科技大学学报, 2011, 35(5):150-155. WANG Panqin, LI Xiaohui. A description method on classification of equipment information based on knowledge factor expression theory[J]. Journal of national university of defense technology, 2011, 35(5):150-155.
[12] 刘增良, 刘有才. 因素神经网络理论及实现策略研究[M]. 北京:北京师范大学出版社, 1992. LIU Zengliang, LIU Youcai. Research on the theory of factor neural network and its realization[M]. Beijing:Research on the Theory of Factor Neural Network and Its Realization, 1992.
[13] 付斌, 李道国, 王慕快. 云模型研究的回顾与展望[J]. 计算机应用研究, 2011, 28(2):420-426.FU Bin, LI Daoguo, WANG Mukuai. Review and prospect on research of cloud model[J]. Application research of computers, 2011, 28(2):420-426.
[14] 张飞舟, 范跃祖, 沈程智, 等. 利用云模型实现智能控制倒立摆[J]. 控制理论与应用, 2000, 17(4):519-523.ZHANG Feizhou, FAN Yuezu, SHEN Chengzhi, et al. Intelligent control inverted pendulum with cloud models[J]. Control theory and application, 2000, 17(4):519-523.
[15] 李德毅, 杜鹢. 不确定性人工智能[M]. 北京:国防工业出版社, 2005:7. LI Deyi, DU Yi. Artificial intelligence with uncertainty[M]. Beijing:National Defend Industry Press, 2005:7.
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