用于异常检测的实值否定选择算法

吉林大学学报(工学版) ›› 2012, Vol. 42 ›› Issue (01): 176-181.

用于异常检测的实值否定选择算法

柴争义^1,2, 王献荣¹, 王亮³

1. 河南工业大学信息科学与工程学院,郑州 450001;
2. 西安电子科技大学计算机学院,西安 710071;
3. 承德医学院物资管理处,河北承德 067000

收稿日期:2010-03-24 出版日期:2012-01-01 发布日期:2012-01-01
作者简介:柴争义(1976-),男,副教授,博士.研究方向:网络安全,计算智能.E-mail:super_chai@tom.com
基金资助:
国家自然科学基金项目(61001202,61003199,61072139);高等学校博士学科点专项科研基金项目(20090203120016,20100203120008);郑州市科技发展计划项目(2010GYXM374);河南省自然科学基金项目(2010A520050).

Real-value negative selection algorithm for anomaly detection

CHAI Zheng-yi^1,2, WANG Xian-rong¹, WANG Liang³

1. School of Information Science and Engineering, He'nan University of Technology, Zhengzhou 450001,China;
2. School of Computer Science and Technology,Xidian University, Xi'an 710071,China;
3. Department of Materials Management, Chengde Medical University, Chengde 067000,China

Received:2010-03-24 Online:2012-01-01 Published:2012-01-01

摘要/Abstract

摘要：

针对已有实值否定选择算法检测器生成过程的不足,提出了一种优化的检测器生成算法。充分利用自体空间的分布,优化检测器生成的中心位置,扩大检测器的半径,尽可能生成覆盖范围大的检测器;使用覆盖率期望值作为算法结束的一个控制参数,有效地避免了冗余检测器的产生。建立了异常检测系统的形式化描述,定义了一个新的异常检测性能衡量指标——错误率。最后,通过人工合成数据集2DSyntheticData以及实际的Iris数据集及Biomedical数据集对算法进行了验证。试验结果表明,相比V-detector算法,本文算法提高了检测率,降低了错误率,减少了所需检测器数量,整体检测性能较优。

关键词: 人工智能, 异常检测, 否定选择算法, 检测器, 检测性能

Abstract:

An optimized detector generation algorithm was proposed to solve the shortcomings of existing real-value negative selection algorithm. This algorithm fully utilizes self distribution, optimizes the center of the detector and expands the radius of the detector in order to generate a detector with large coverage. The expected coverage was used as one of the control parameters to end the algorithm so that it can effectively avoid the generation of redundant detector. Moreover, the anomaly detection system and it formal description were established. The error rate as a new index for anomaly detection performance was defined. The 2DSyntheticData and the actual Irish data set and biomedical data were used to test the algorithm. Experimental results show that, compared with V-detector, the algorithm improves detection rate, reduces the error rate and the required number of detectors. So it has a better detection performance.

Key words: artificial intelligence, anomaly detection, negative selection algorithm, detector, detection performance

中图分类号:

TP18

柴争义, 王献荣, 王亮. 用于异常检测的实值否定选择算法[J]. 吉林大学学报(工学版), 2012, 42(01): 176-181.

CHAI Zheng-yi, WANG Xian-rong, WANG Liang. Real-value negative selection algorithm for anomaly detection[J]. 吉林大学学报(工学版), 2012, 42(01): 176-181.

参考文献

[1] 朱思峰,刘芳,柴争义. 基于检测器覆盖率评估的否定选择算法
[J]. 华中科技大学学报:自然科学版,2009,37(12):1407-1410. Zhu Si-feng, Liu Fang,Chai Zheng-yi. A novel negative selection algorithm using detector's coverage evaluation
[J]. Journal of Huazhong University of Science and Technology(Nature Science Edition), 2009,37(12):1407-1410.

[2] Bereta M,Burczy'nski T. Comparing binary and real-valued coding in hybrid immune algorithm for feature selection and classification of ecg signals
[J]. Eng Appl Artif Intell, 2007,20(5):571-585.

[3] González F, Dasgupta D, Kozma R. Combining negative selection and classification techniques for anomaly detection//Proceedings of the 2002 Congress on Evolutionary Computation CEC2002.USA:IEEE Press,2002:705-710.

[4] González F A,Dasgupta D. Anomaly detection using real-valued negative selection
[J]. Journal of Genetic Programming and Evolvable Machines,2003,4(4):383-403.

[5] González F, Dasgupta D,Nio L F. A randomized rea-valued negative selection algorithm
[J]. Lecture Notes in Computer Science,2003,2787:261-272.

[6] Zhou Ji, Dasgupta D. Real-valued negative selection algorithm with variable-sized detectors//Proceedings of GECCO. Berlin: Springer, 2005: 287-298.

[7] Zhou Ji. A boundary-aware negative selection algorithm//IASTED International Conference on Artificial Intelligence and Soft Computing (ASC), Benidorm, Spain, 2006:45-50.

[8] Zhou Ji, Dasgupta D. V-Detector: an efficient negative selection algorithm with "probably adequate" detector coverage
[J]. Information Sciences,2009,17(9):1390-1406.

[9] Zhang Feng-bin, Wang Da-wei, Wang Sheng-wen. A self region based real-valued negative selection algorithm
[J]. Journal of Harbin Institute of Technology, 2008,15(6):851-855.

[10] Zeng Jin-quan, Liu Xiao-jie,Tao Li. A self-adaptive negative selection algorithm used for anomaly detection
[J]. Progress in Natural Science,2009,19(2),261-266.

[11] 柴争义,刘芳,朱思峰. 新型智能入侵防御模型
[J]. 华中科技大学学报:自然科学版,2010,38(1):22-24. Chai Zheng-yi., Liu Fang, Zhu Si-feng. Novel intelligent model for intrusion prevention
[J]. Journal of Huazhong University of Science and Technology(Nature Science Edition),2010,38(1):22-24.

[12] 刘衍珩,孙蕾,田大新,等. 基于融合主成分匹配的异常检测方法
[J]. 吉林大学学报:工学版,2009,39(5):1314-1320. Liu Yan-heng,Sun Lei,Tian Da-xin, et al. Method of anomaly detection based on fusion principal components match
[J]. Journal of Jilin University(Engineering and Science Edition):2009,39(5):1314-1320.

[13] 马鑫,梁艳春,田野,等. 基于免疫机理与合同网协议的多Agent入侵检测系统
[J]. 吉林大学学报:工学版,2011,41(1):176-181. Ma Xin,Liang Yan-chun,Tian Ye,et al. An immune and contact net protocol-based multi-agent lintrusion detection system
[J]. Journal of Jilin University(Engineering and Science Edition):2011,41(1):176-181.

[14] Columbia University. 2D Synthetic Data.. http://www. zhouji. net/prof/2DSyntheticData.zip.

[15] StatLib Datasets Archive. http://lib.stat.cmu.edu//dataset.

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