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Network security situational assessment method based on improved D-S evidence theory


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Network security situational assessment method based on improved D-S evidence theory
Tang YongliLi WeijieYu JinxiaYan Xixi
School of Computer Science and Technology,Henan Polytechnic University,Jiaozuo 454000,China
network security situational assessment back propagation neural network D-S evidence theory basic probability assignation situational recognition rate
It is a hot issue for network security situational assessment in the field of information security.In order to solve the problem of over-reliance on expert experience,it proposes a security situational assessment method based on the improved D-S evidence theory.For this method,it fuses multi-source situation information and uses the back propagation(BP)neural network based on Genetic Algorithm to obtain the basic probability assignation(BPA)of the D-S evidence theory.The D-S evidence theory is adopted to integrate the BPA in turn,weaken the interference of artificial factors on BPA,and improve the BPA forecasting accuracy and the situational recognition rate of network security situation.Tests with a real network environment show that this method effectively improves the network security situational assessment.


[1] Bass T.Intrusion systems and multi-sensor data fusion.creating cyberspace situational awareness[J].Communications of the ACM,2000,43(4):99-105.
[2]Holsopple J,Yang S J.FuSIA:Future situation and impact awareness[A].Proceedings of the 11th International Conference on Information Fusion[C].Cologne,Germany:IEEE,2008:1-8.
[3]Holsopple J,Sudit M,Nusinov M,et al.Enhancing situation awareness via automated situation assessment[J].IEEE Communications Magazine,2010,48(3):146-152.
[4]Liu Z,Li S J,He J,et al.Complex network security analysis based on attack graph model[A].2012 Instrumentation,Measurement,Computer,Communication and Control International Conference[C].Harbin,China:IEEE Computer Society,2012:183-186.
[5]Zhang S,Yao S,Ye X,et al.A network security situation analysis framework based on information fusion[A].Proceedings of 6th IEEE Joint International Information Technology and Artificial Intelligence Conference[C].Chongqing,China:IEEE Computer Society,2011:362-332.
Gong Zhenghu,Zhuo Ying.Research on cyber space situational awareness[J].Journal of Software,2010,21(7):1605-1619.
Wei Yong,Lian Yifeng,Feng Dengguo.A network security situational awareness model based on information fusion[J].Journal of Computer Research and Development,2009,46(3):353-362.
Wu Di,Lian Yifeng,Chen Kai,et al.A security threats identification and analysis method based on attack graph[J].Chinese Journal of Computers,2012,35(9):1939-1950.
Yang Yahui,Huang Haizhen,Shen Qingni,et al.Research on intrusion detection based on incremental GHSOM[J].Chinese Journal of Computers,2014,37(5):1216-1224.
Zhao Qiuyue,Zuo Wanli,Tian Zhongsheng,et al.A method for assessment of trust relationship strength based on the improved D-S evidence theory[J].Chinese Journal of Computers,2014,37(4):874-883.
Chen Xiuzhen,Zheng Qinghua,Guan Xiaohong,et al.Quantitative hierarchical threat evaluation model for network security[J].Journal of Software,2006,17(4):885-897.
Yan Pingfan,Zhang Changshui,et al.Artificial neural networks and simulated evolutionary computation[M].Beijing:Tsinghua University Press,2005.
Chen Debao,Zhao Chunxia.Design and application of WRBF neural network based on improved GA[J].Journal of Nanjing University of Science and Technology,2007,31(3):370-374.
Xie Lixia,Wang Yachao,Yu Jinbo.Network security situation awareness based on neural networks[J].Journal of Tsinghua University,2013,53(12):1750-1760.


Last Update: 2015-08-31