计算机科学 ›› 2015, Vol. 42 ›› Issue (6): 167-170.doi: 10.11896/j.issn.1002-137X.2015.06.036

• 信息安全 • 上一篇    下一篇

基于免疫危险理论的网络安全态势评估

陈妍伶,汤光明,孙怡峰   

  1. 解放军信息工程大学 郑州450001,解放军信息工程大学 郑州450001,解放军信息工程大学 郑州450001
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受河南省科技攻关计划(122102210047)资助

Assessment of Network Security Situation Based on Immune Danger Theory

CHEN Yan-ling, TANG Guang-ming and SUN Yi-feng   

  • Online:2018-11-14 Published:2018-11-14

摘要: 为实时定量评估网络安全态势,提出了一种基于免疫危险理论的网络安全态势评估方法。通过研究免疫运行机制,定义了网络安全问题中的抗原、抗体和免疫细胞,描述了危险信号的判断规则,准确识别出了抗原。在分析免疫应答机制和免疫平衡机制中抗体浓度变化原因的基础上,给出了抗体浓度的计算方法。最后,结合抗体浓度与危险程度的关系,建立了基于抗体浓度的危险感知模型以实时定量评估网络安全态势。仿真实验表明,所提方法计算出的抗体浓度准确地反映了系统面临的危险程度,能够为网络管理提供有效的决策支持。

关键词: 危险理论,人工免疫,抗体浓度,网络安全,态势评估

Abstract: In order to assess network security situation in real-time and quantification,an assessment method based on immune danger theory was proposed.Through studying the immune operation mechanism,antigen,antibody and immune cell in the problem of network security were defined.On the premise of describing the judgment rules of danger signal,the antigen is recognized accurately.Based on the changes of antibody density in the immune response and immune balance mechanisms,the calculation method of antibody density was given.Finally,by analyzing the relationship between antibody density and danger level,a danger awareness model based on antibody density was built to assess network security situation in real-time and quantification.The simulation results show that antibody density calculated by using the proposed method accurately reflects the danger level that the system faces,which can provide effective decision-making support for network management.

Key words: Danger theory,Artificial immune,Antibody density,Network security,Situation evaluation

[1] Bass T,Arbor A.Multi-sensor data fusion for next generation distributed intrusion detection systems[C]∥Proceeding of IRIS National Symposium on Sensor and Data Fusion.Laurel,MD:[s.n.],1999:24-27
[2] Wang Ling-yu,Singhal A,Jajodia S.Measuring network security using attack graphs[C]∥Proceedings of the 2007 ACM Workshop on Quality of Protection.New York:ACM Press,2007:49-54
[3] Ning Peng,Cui Yun,Reeves D S,et al.Techniques and tools for analyzing intrusion alerts[J].ACM Transactions on Information and System Security,2004,7(2):274-318
[4] 郑黎明,邹鹏,张建锋,等.面向大规模网络的安全态势实时量化感知模型[J].计算机科学,2011,8(10):30-35 Zheng Li-ming,Zou Peng,Zhang Jian-feng,et al.Real time situational awareness model for large-scale networks[J].Computer Science,2011,38(10):30-35
[5] 张勇,谭小彬,崔孝林,等.基于Markov博弈模型的网络安全态势感知方法 [J].软件学报,2011,2(3):495-508 Zhang Yong,Tan Xiao-bin,Cui Xiao-lin,et al.Network Security Situation Awareness Approach Based on Markov Game Model[J].Journal of Software,2011,2(3):495-508
[6] 卓莹,何明,龚正虎.网络态势评估的粗集分析模型[J].计算机工程与科学,2012,4(3):1-5 Zhuo Ying,He Ming,Gong Zheng-hu.A Rough Set Analysis Model of Network Situation Awareness[J].Computer Enginee-ring & Science,2012,4(3):1-5
[7] Feng Xue-wei,Wang Dong-xia,et al.Security Situation Assessment Based on the DS Theory[C]∥Proceedings of the 2nd International Workshop on Education Technology and Computer Science.Wuhan,China:IEEE Comput.Soc,2010:352-356
[8] Matzinger P.The Danger Model:a Renewed Sense of Self[J].Science,2002(12):301-305
[9] Dasgupta D,Yu S,Nino F.Recent Advances in Artificial Im-mune Systems:Models and Applications[J].Applied Soft Computing,2011(11):1574-1587
[10] Yin Meng-jia,Zhang Tao,Shu Yuan.An Artificial ImmuneModel with Danger Theory Based on Changes[C]∥Proceedings of 2011 IEEE International Conference on Information Theory and Information Security.Wuhan:Wuhan University,2012:672-676
[11] 张永铮,云晓春.网络运行安全指数多维属性分类模型[J].计算机学报,2012,5(8):1666-1678 Zhang Yong-zheng,Yun Xiao-chun.Network Operation Security Index Classification Model with Multidimensional Attributes[J].Chinese Journal of Computers,2012,5(8):1666-1678

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!