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V-detector优化算法 被引量：4

A V-detector optimization algorithm
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摘要 针对人工免疫系统中V-detector否定选择算法造成的检测器集合黑洞和检测器高重叠率等问题，借鉴生物免疫系统对免疫细胞的调节机制，提出了V-detector优化算法。该算法从父代产生候选检测器子代并通过检测器之间以及检测器与自体集合之间的亲和力对比更新检测器集合，使得检测器集合对非自体空间的覆盖更加合理。通过二维仿真实验和KDDCUP99数据集实验测试，经优化后的检测器集合对非自体空间的覆盖性能有了显著提高，有效提高了系统的检测性能。 An optimization algorithm for variable-coverage detectors （V-detectors） was designed based on the immunecell regulation mechanism in biology immune systems to solve the problems of V-detector hole and high V-detector overlapping of the V-detector algorithm, a real-valued negative selection algorithm with V-detectors. The algorithm updates the detector set by the candidates generated from their parents and the affinity comparison to improve detectors＇ distribution performance. It was tested by the synthetic data and the KDD CUP 99 data set. The results show that the optimized detectors can increase the efficiency of detectors＇ distribution and improve the system＇ s detection performance.
作者 张凤斌 席亮 王胜文 岳新
机构地区 哈尔滨理工大学计算机科学与技术学院
出处 《高技术通讯》 CAS CSCD 北大核心 2012年第5期449-454,共6页 Chinese High Technology Letters
基金 国家自然科学基金（60671049,61172168）资助项目.
关键词 人工免疫系统 实值 可变阈值否定选择算法 优化算法 artificial immune system, real-valued, negative selection algorithm with variablecoverage detec-tors, optimization algorithm
分类号 TP393 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献18

  • 1Dasgupta D, Zhou J, Gonzalez F. Artificial immune sys- tem (AIS) research in the last five years. In: Proceed- ings of the IEEE Congress on Evolutionary Computation 2003, Canberra, Australia, 2003. 123-130.
  • 2Dal D, Abraham S, Abraham A, et al. Evolutionary in- duced secondary immunity: an artificial immune systems based intrusion detection systems. In: Proceedings of the 7th Computer Information Systems and Industrial Manage-ment Applications 2008, Ostrava Czech Republic, 2008. 65 -70.
  • 3Dasgupta D, Yu S, Nino F. Recent Advances in artificial immune systems: models and applications. Applied Soft Computing, 2011, 11 (2) : 1574-1587.
  • 4Barreira A F, Teixeira O N, De J G, et al. Evolutionary artificial immune system optimization. In: Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, Portland, USA, 2010. 2065-2066.
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  • 6Dasgupta D, Saha S. Password security through negative filtering. In: Proceedings of 2010 International Confer- ence on Emerging Security Technologies, Canterbury, UK, 2010. 83-89.
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  • 9Gonzalez F, Dasgupta D. Anomaly detection using real- valued negative selection. Genetic Programming and Evolvable Machines, 2003, 4(4) : 383-403.
  • 10Zhou J, Dasgupta D. Applicability issues of the real-val- ued negative selection algorithms. In: Proceedings of the Conference on Genetic and Evolutionary Computation 2006, Seattle, USA, 2006. 111-118.

二级参考文献6

  • 1Fabio G,Dasgupta D.Anomaly detection using real-valued negative selection[J].Genetic Programming and Evolvable Machines,2003,23(3):383-403.
  • 2Ji Z,Dasgupta D.Real-valued negative selection algorithm with variable-sized detectors[C]∥Genetic and Evolutionary Computation.Seattle:IEEE Press,2004:287-298.
  • 3Ji Z,Dasgupta D.Estimating the detector coverage in a negative selection algorithm[C]∥Genetic and Evolu-tionary Computation.Washington:IEEE Press,2005:88-97.
  • 4Gu G F,Sharif M,Qin X Z.Worm detection,early warning and response based on local victim information[C]∥Proc of the 20th Annual Computer Security Applications Conference.Arizona:[s.n.],2004:136-145.
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  • 6Zou C,Gao L,Gong W.Monitoring and early warning for Internet worms[C]∥Proc of the ACM Conference on Computer and Communications Security.Washington:ACM Press,2003:190-199.

共引文献5

同被引文献68

  • 1Kim J, Bentley P J. The Human Immune System and Network Intrusion Detection[C]//Proc. of the 7th European Congress on Intelligent Techniques and Soft Computing. Aachen, Germany: [s. n.], 1999.
  • 2Zhou Ji, Dasgupta D. V-Detector: An Efficient Negative Selection Algorithm with “Probably Adequate” Detector Coverage[J]. Information Sciences: An International Journal, 2009, 179(10): 1390-1406.
  • 3Zhou Ji, Dasgupta D. Revisiting Negative Selection Algori- thms[J]. Evolutionary Computation, 2007, 15(2): 223-251.
  • 4Gonzalez F, Dasgupta D, Kozema D. Combining Negative and Classification Techniques for Anomaly Detection[C]//Proc. of Congress on Evolutionary Computation. Honolulu, USA: IEEE Press, 2002.
  • 5Balachandran S, Dasgupta D, Nino F, et al. A Framework for Evolving Multi-shaped Detectors in Negative Selection[C]// Proc. of IEEE Symposium on Foundations of Computational Intelligence. Honolulu, USA: IEEE Press, 2007.
  • 6Xu Bin, Zhuang Yi. Hybrid Detector Based Negative Selection Algorithm[C]//Proc. of the 5th International Conference on Wireless Communications Networking and Mobile Computing. Beijing, China: [s. n.], 2009.
  • 7Violato R P, Azzolini A G. Antibodies with Adaptive Radius as Prototypes of High-dimensional Datasets[C]//Proc. of the 9th International Conference on Artificial Immune Systems. Edinburgh, UK: [s. n.], 2010.
  • 8Zhou Ji, Dasgupta D. Estimating the Detector Coverage in a Negative Selection Algorithm[C]//Proc. of Conference on Genetic and Evolutionary Computation. Washington D. C., USA: [s. n.], 2005.
  • 9UCI Machine Learning Repository. Fisher’s Iris Data[EB/OL]. (2009-12-23). http://archive.ics.uci.edu/ml/datasets/Iris.
  • 10ZHENG X F, FANG Y H, ZHOU Y H, et al. A Novel Multi-lay- ered Immune Network Intrusion Detection Defense Model: MINID [J]. Journal of Networks, 2013, 8(3): 636-644.

引证文献4

二级引证文献8

高技术通讯
2012年 第5期

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