A Perception Model for Network Risk Using Danger-Theory

doi:10.13190/jbupt.201003.40.034

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2010, Vol. 33 ›› Issue (3): 40-43.doi: 10.13190/jbupt.201003.40.034

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A Perception Model for Network Risk Using Danger-Theory

CHAI Zheng-yi^1,2, LIU Fang¹

1School of Computer Science and Technology, Xidian University, Xian 710071, China; 
2School of Information Science and Engineering, Hennan University of Technology, Zhengzhou 450001, China)

Received:2009-07-06 Revised:2010-03-09 Online:2010-06-28 Published:2010-05-14

Abstract

Abstract:

In order to effectively assess the risk of network risk, and to take effective prevention measures,

featuring with realtime and quantitative detection, a dangertheory and Antibodyconcentration based perception model for network risk is proposed. Based on dynamic selfsets, the mathematical model and dynamic equations for selftolerance and various antibody are

designed. The quantify description of antibody concentration is given. The candidate detector

is generated from both random and detector genelib. Furthermore, the vaccination

are introduced to enhance the active detection. Simulations are done to test the model. The

experiments prove that the model can detect the intrusion attacks effectively and evaluate the

risk of both host and network including each attack and the whole attacks. If the model is trans-formed properly, it also can be applied to the fields of virus detection and spam mail recognition.

Key words: artificial immune system, danger theory, network risk, real-time detection, a ntibody concentration

CLC Number:

TP393

CHAI Zheng-yi; LIU Fang;.

A Perception Model for Network Risk Using Danger-Theory [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2010, 33(3): 40-43.

References

[1] Visintine V. An introduction to information risk assessment. SANS Institute Journal, 2003, 8(5): 101-118.
[2] Chu C K, Chu M. An integrated framework for the assessment of network operations, reliability, and security[J]. Bell Labs Technical Journal, 2004, 8(4): 133-152.
[3] 王晓箴, 卢志刚, 刘宝旭. 结合OCTAVE和灰色系统的信息安全风险评估方法[J]. 北京邮电大学学报, 2009, 32(5): 128-131. Wang Xiaozhen, Lu Zhigang, Liu Baoxu. Algorithm of information security risk evaluation based on OCTAVE and grey system [J]. Journal of Beijing University of Posts and Telecommunications, 2009, 32(5): 128-131.
[4] 韦勇, 连一峰. 基于日志审计与性能修正算法的网络安全态势评估模型. 计算机学报, 2009, 32(4): 763-772. Wei Yong, Lian Yifeng. A network security situational awareness model based on log audit and performance correction[J]. Chinese Journal of Computer, 2009, 32(4): 763-772.
[5] Li Tao. An immunity based network security risk estimation[J]. Science in China Ser F Information Sciences, 2005, 48(5): 557-578.
[6] 彭凌西, 陈月峰. 基于危险理论的网络风险模型[J]. 电子科技大学学报, 2007, 36(6): 1198-1201. Peng Lingxi, Chen Yuefeng. Danger theory based network risk evaluation model. Journal of University of Electronic Science and Technology of China, 2007, 36(6): 1198-1201.
[7] Glickman M, Balthrop J, Forrest S. A machine learning evaluation of an artificial immune system[J]. Evolut Comput, 2008, 13(2): 179-212.
[8] Matzinger P. Danger model: a renewed sense of self[J]. Science, 2004, 296(5566): 301-305.
[9] Zhou Ji, Dasgupta D. Revisiting negative selection algorithm[J]. Evolutionary Computation Journal, 2007, 15(2): 123-139.
[10] Dasgupta D. Advances in artificial immune system[J]. IEEE Comp Intel Mag, 2008, 11(4): 4-9.
[11] Kim J, Bentley P. Immune memory and gene library evolution in dynamical clone selection algorithm[J]. Journal of Genetic Programming and Evolvable Machines, 2008, 5(4): 361-391.
[12] Li Tao. Dynamic detection for computer virus based on immune system [J]. Science in China Series F: Information Sciences, 2008, 51(10): 1475-1486.
[13] University of California. KDDLib. (2009-03-02). http://kdd.ics.uci.edu/databases/kddcup99.html.

A Perception Model for Network Risk Using Danger-Theory

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