态势感知中的数据融合和决策方法综述

doi:10.3969/j.issn.1000-3428.2014.05.005

摘要/Abstract

摘要： 在赛博空间态势感知的相关研究中，处理不确定、不精确的多源异构信息是态势认识过程中需要解决的一个重要问题。为正确处理这些信息，提高对态势的认识，使得到的态势更具有正确性、时效性和全局性，研究数据融合方式和决策方式等现存的处理技术并进行综述。数据融合包含贝叶斯网络、D-S证据理论、粗糙集理论、神经网络、隐马尔科夫模型及马尔科夫博弈论等方式，决策方式涵盖认知心理学、逻辑学、风险管理等。研究结果表明，目前的技术焦点呈现多样性，但在态势生成应用及验证方面仍有较大的改进空间。

关键词: 赛博空间, 态势感知, 多源异构, 数据融合, 决策

Abstract: In the research of cyberspace situation awareness, how to deal with uncertain, inaccurate multi-source heterogeneous information is an important problem which needs to be solved in the process of situational understanding. In order to accurately handle with the information, improve the awareness of the situation, make the situation more accuracy, timeliness and overall, the paper reviews the existing technology focus, mainly including data fusion methods and decision-making methods. Data fusion methods mainly includes Bayesian network, D-S evidence theory, rough set theory, neural network, hidden Markov model and Markov game theory methods, and decision-making mainly includes cognitive psychology, logic and risk management methods. Research results show that current technology focuses present diversity, but still has great space for improvement in both the situation generation application and verification.

Key words: cyberspace, situation awareness, multi-source heterogeneous, data fusion, decision-making

中图分类号:

TP311.13

盖伟麟，辛丹，王璐，刘欣，胡建斌. 态势感知中的数据融合和决策方法综述[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2014.05.005.

GAI Wei-lin, XIN Dan, WANG Lu, LIU Xin, HU Jian-bin. Review of Date Fusion and Decision-making Methods in Situation Awareness[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2014.05.005.

http://www.ecice06.com/CN/Y2014/V40/I5/21

参考文献

参考文献 [1] 王慧强, 赖积保, 朱亮, 等. 网络态势感知系统研究综述[J]. 计算机科学, 2006, 33(10): 5-10. [2] Hall D L, Llinas J. An Introduction to Multisensor Data Fusion[J]. Proceedings of the IEEE, 1997, 85(1): 6-23. [3] Salerno J. Information Fusion: A High-level Architecture Overview[C]//Proc. of the 5th International Conference on Information Fusion. Annapolis, USA: IEEE Press, 2002: 680- 686. [4] 张连文, 郭海鹏. 贝叶斯网引论[M]. 北京: 科学出版社, 2006. [5] 罗文, 李敏勇, 张晓锐. 基于贝叶斯网络的态势认识[J]. 火力与指挥控制, 2010, 35(3): 89-92. [6] 周志强, 张晓燕. 基于贝叶斯网络的博弈融合态势评估方法[J]. 计算机与数字工程, 2008, 36(10): 17-19. [7] 戴亚平, 刘征, 郁光辉. 多传感器数据融合理论及应用[M]. 北京: 北京理工大学出版社, 2004. [8] 诸葛建伟, 王大为, 陈昱, 等. 基于D-S证据理论的网络异常检测方法[J]. 软件学报, 2006, 17(3): 463-471. [9] Qu Zhaoyang, Li Yaying, Li Peng. A Network Security Situation Evaluation Method Based on D-S Evidence Theory[C]//Proc. of ESIAT’10. Wuhan, China: [s. n.], 2010: 496-499. [10] 苗夺谦, 李道国. 粗糙集理论, 算法与应用[M]. 北京: 清华大学出版社, 2008. [11] 梁颖, 王慧强, 赖积保. 一种基于粗糙集理论的网络安全态势感知方法[J]. 计算机科学, 2007, 34(8): 95-97. [12] Meng Guanglei, Gong Guanghong. Algorithm of Battlefield Situation Assessment Based on Bayesian Network and Rough Set Theory[C]//Proc. of the 7th International Conference on System Simulation and Scientific Computing. Beijing, China: [s. n.], 2008: 776-779. [13] Wang Xiaofan, Wang Baoshu. Methods for Situation Assess- ment of Multi-attribute Decision Making Based on Rough Sets[C]//Proc. of the 6th International Conference on Fuzzy Systems and Knowledge Discovery. Tianjin, China: [s. n.], 2009: 325-328. [14] 韩力群. 人工神经网络教程[M]. 北京: 北京邮电大学出版社, 2006. [15] 任伟, 蒋兴浩, 孙锬锋. 基于RBF神经网络的网络安全态势预测方法[J]. 计算机工程与应用, 2006, 42(31): 136- 138. [16] 孟锦, 马驰, 何加浪, 等. 基于HHGA-RBF神经网络的网络安全态势预测模型[J]. 计算机科学, 2011, 38(7): 70-72. [17] Lai Jibao, Wang Huiqiang, Liu Xiaowu, et al. A Quantitative Prediction Method of Network Security Situation Based on Wavelet Neural Network[C]//Proc. of the 1st International Symposium on Data, Privacy, and E-commerce. Chengdu, China: [s. n.], 2007: 197-202. [18] Wang Huiqiang, Liu Xiaowu, Lai Jibao, et al. Network Security Situation Awareness Based on Heterogeneous Multi-sensor Data Fusion and Neural Network[C]//Proc. of the 2nd International Multi-symposiums on Computer and Computational Sciences. Iowa, USA: IEEE Press, 2007: 352-359. [19] Rabiner L R. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition[J]. Proceedings of the IEEE, 1989, 77(2): 257-286. [20] Shen Dan, Chen Genshe, Cruz J B, et al. A Markov Game Theoretic Data Fusion Approach for Cyber Situational Awareness[C]//Proc. of IEEE Military Communications Conference. Orlando, USA: [s. n.], 2007: 1-7. (下转第30页) (上接第25页) [21] Zhang Qiang, Man Dapeng, Yang Wu. Using HMM for Intent Recognition in Cyber Security Situation Awareness[C]//Proc. of the 2nd International Symposium on Knowledge Acquisition and Modeling. Wuhan, China: [s. n.], 2009: 166-169. [22] Endsley M R. Toward a Theory of Situation Awareness in Dynamic Systems[J]. Human Factors: The Journal of the Human Factors and Ergonomics Society, 1995, 37(1): 32-64. [23] Jajodia S, Liu P, Swarup V, et al. Cyber Situational Awareness: Issues and Research[M]. [S. l.]: Springer, 2010. [24] Ammann P, Wijesekera D, Kaushik S. Scalable, Graph-based Network Vulnerability Analysis[C]//Proc. of the 9th ACM Conference on Computer and Communications Security. New York, USA: ACM Press, 2002: 217-224. [25] Cheung S, Lindqvist U, Fong M W. Modeling Multistep Cyber Attacks for Scenario Recognition[C]//Proc. of DARPA Information Survivability Conference and Exposition. Washington D. C., USA: IEEE Press, 2003: 284-292. [26] Mell P, Scarfone K, Romanosky S. A Complete Guide to the Common Vulnerability Scoring System Version 2.0[M]. [S. l.]: FIRST.org, Inc., 2007. 编辑任吉慧

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