期刊文献+

免疫检测模型中检测器的激励响应机制研究 被引量:3

The Research of the Stimulation and Response Mechanism of Detector in Immune-Based Detection Model
下载PDF
导出
摘要 文章研究与借鉴免疫系统中B细胞的生成、演化和检测的过程,针对当前IDS免疫模型研究中存在的不足,提出了检测器在不同检测阶段中的演变形式和行为方式,并给出了检测器的异常触发、活化概率函数以及相互间激励或抑制的算法与机制,同时针对活化响应发生链式反应、检测器的识别域之间交迭覆盖以及检测器规模等问题,建立了优先权和率先活化机制.实验结果验证了检测器激励响应机制一方面实现了检测器动态更新和联想记忆的功能,另一方面摒弃了盲目地随机产生检测器的传统方式,提高了免疫检测模型的自学习和自适应的能力. With the inspiration of the process of naive B-cells' generation, evolution and detec tion, the paper designs the evolving forms and action manners of detectors in different detection stages according to the limitation existed in current immune based IDSs. Meanwhile a mechanism of Abnormity Triggering and the probability functions for stimulation and restraint are given in detail. In order to solve some problems such as chain response, overlapping and detectors' size, a mechanism of Prior Order & Prior Activation is built. The experimental results demonstrate the novel mechanism can not only realize the dynamic update and association memory of detectors, but also discard the traditional manner to produce detectors blindly and randomly, which improves the adaptability and self-learning of immune-baSed detection model.
出处 《计算机学报》 EI CSCD 北大核心 2006年第11期1929-1936,共8页 Chinese Journal of Computers
基金 公安部科研项目基金(200342-823-01) 中南财经政法大学科研启动金(00008673 XXXYKT2006003)资助.
关键词 入侵检测 自然免疫系统 激励响应 联想记忆 异常触发 intrusion detection natural immune system stimulation and response association memory abnormity triggering
  • 相关文献

参考文献8

  • 1Midori Asaka,Takefumi,Shigeki.Public information server for tracing intruders in the Internet.IEICE Transactions on Communication,2001,E84-B(12):3104~3111
  • 2Lippmann Richard,Haines Joshua W..The 1999 DARPA off-line intrusion detection evaluation.Computer Networks,2000,34(4):579~595
  • 3Forrest S.,Allen L.,Perelson A.S.,Cherukuri R..Self-nonself discrimination in a computer.In:Proceedings of the IEEE Symposium on Research in Security and Privacy,1994,1:202~212
  • 4Hofmey Steven Andrew.An immunological model of distributed detection and its application to computer security[Ph.D.dissertation].Department of Computer Sciences,University of New Mexico,Albuquerque,1999
  • 5闫巧,江勇,吴建平.基于免疫机理的网络入侵检测系统的抗体生成与检测组件[J].计算机学报,2005,28(10):1601-1607. 被引量:18
  • 6Gonzales F.,Dasgupta D..Anomaly detection using real-valued negative selection.Journal of Genetic Programming and Evolvable Machines,2003,4(4):383~403
  • 7Caudell T.,Newman D..An adaptive resonance architecture to define normality and detect novelties in time series and databases.In:Proceedings of the IEEE World Congress on Neural Networks,Portland,Oregon,1993,4:166~176
  • 8Lippmann R.,Haines J.W..The 1999 Darpa off-line int rusiondetection evaluation.Computer Networks,2000,34(4):579~595

二级参考文献10

  • 1Hofmeyr S.A.. An interpretative introduction to the immune system. In: Cohen I., Segel L. eds.. Design Principles for the Immune System and Other Distributed Autonomous Systems. England: Oxford University Press, 2000.
  • 2Roesch M. Snort- lightweight intrusion detection for networks. In: Proceedings of the 13th USENIX Conference on System Administration, Seattle, Washington, 1999, 229~238.
  • 3Forrest S., Perelson A., Allen L.. Self-noself discrimination in a computer. In: Proceedings of the 1994 IEEE Symposium on Researchin Security and Priracy, Los Alamos, CA, 1994.
  • 4Kim J., Bentley J.P.. An evaluation of negative selection in an artificial immune system for network intrusion detection. In: Proceedings of the Genetic and Evolutionary Computation Conference 2001(GECCO-2001), San Francisco, 2001,  1330~1337.
  • 5Axelsson S.. The base-rate fallacy and its implications for the difficulty of intrusion detection. In: Proceedings of the 6th ACM Conference on Computer and Communications Security, Kent Ridge Digital Labs, Singapore, 1999, 1~7.
  • 6Lippmann R., Haines J.W.. The 1999 Darpa off-line intrusion detection evaluation. Computer Networks, 2000, 34(4): 579~595.
  • 7Harmer P.K.,Williams P.D., Gunsch G.H., Lamont G.B.. An artificial immune system architecture for computer security applications. IEEE Transactions on Evolutionary Computation, 2002, 6(3): 252~280.
  • 8Hofmeyr S.A.. An immunological model of distributed detection and its application to computer security[Ph.D. dissertation]. University of New Mexico, New Mexico, 1999.
  • 9Dasgupta D., Gonzalez F.. An immunity-based technique to characterize intrusions in computer networks. IEEE Transactions on Evolutionary Computation, 2002, 6(3): 281~291.
  • 10Kim J.. An artificial immune system for network intrusion detection. In: Proceedings of the 7th European Congress on Intelligent Techniques and Soft Computing(EUFIT'99), Aachen, Germany, 1999.

共引文献17

同被引文献30

  • 1LI Tao.An immunity based network security risk estimation[J].Science in China(Series F),2005,48(5):557-578. 被引量:30
  • 2LI Tao.An immune based dynamic intrusion detection model[J].Chinese Science Bulletin,2005,50(22):2650-2657. 被引量:17
  • 3李涛.基于免疫的网络监控模型[J].计算机学报,2006,29(9):1515-1522. 被引量:53
  • 4Su Purui, Feng Detlgguo. The Design of an Artificial Immune System[C]//Proc. of International Conference on Networking, Systems, Mobile Communications and Learning Technologies. [S. l.]: IEEE Press, 2006: 1076-1087.
  • 5Sarafijanovic S, Boudec J Y. An Artificial Immune System Approach with Secondary Response for Misbehavior Detection in Mobile Ad Hoc Networks[J]. IEEE Transactions on Neural Networks, 2005, 16(5): 1076-1087.
  • 6Shadbolt N.Ambient intelligence[C]//IEEE Transactions on Intelligent Transportation Systems, 2003,18 (4) : 2-3.
  • 7Su Pu-rui,Feng Deng-guo.The design of an artificial immune system[C]//International Conference on Networking,International Conference on Systems and International Conference on Mobile Communications and Learning Technologies(ICNICONSMCL'06),Mauritius, 2006,195 : 1076-1087.
  • 8Sarafijanovic S,Le Boudec J Y.An artificial immune system approach with secondary response for misbehavior detection in mobile Ad hoc networks[J].IEEE Transactions on Neural Networks, 2005,16(5): 1076-1087.
  • 9de Castro L N, Timmis J. Artificial Immune Systems: A New Computational Intelligence Approach[M]. London, UK: Springer-Verlag, 2002.
  • 10He Yang, Liang Yiwen, Li Tao, et al. A Method Inspired from Differential Coefficient for Calculating Danger Signals in Artificial Immune System[C]//Proc. of PACIIA’09. Wuhan, China: [s. n.], 2009.

引证文献3

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部