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基于遗传禁忌搜索的分类器选择集成方法 被引量:1

Classifier Selection Integrated Method Based on Genetic Tabu Search
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摘要 为提高入侵检测的精度,提出一种使用遗传禁忌搜索的分类器选择集成方法。该方法采用Bagging算法构建初始分类器集合,根据遗传禁忌搜索算法选择分类器子集,以该子集建立多分类器系统进行入侵检测。实验结果表明,与Bagging算法相比,该方法能有效提高检测精度、降低误报率。 In order to improve the accuracy of intrusion detection,this paper puts forward a classifier integration method which uses tabu genetic search.It uses Bagging algorithm to found classifiers.It adopts tabu genetic search to select classifier,and uses the selected classifiers to establish an intrusion detection system.Experimental results show that compared with Bagging algorithm,this method can improve detection accuracy and reduce false alarm rate.
作者 张永 朱林杰
出处 《计算机工程》 CAS CSCD 北大核心 2011年第8期183-185,共3页 Computer Engineering
基金 甘肃省自然科学基金资助项目(0809RJZA015)
关键词 分类器 遗传禁忌搜索 BAGGING算法 遗传算法 classifier; genetic tabu search; Bagging algorithm; genetic algorithm;
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参考文献11

  • 1Holland J. Adaptation in Natural and Artificial Systems[M]. Michigan, USA: University of Michigan Press, 1975.
  • 2Wong Man Sing, Yan Wai Yeung. Investigation of Diversity and Accuracy in Ensemble of Classifiers Using Bayesian Decision Rules[C]//Proc. of EORSA'08. Beijing, China: [s. n.], 2008.
  • 3Panda M, Patra M R. Ensemble of Classifiers for Dctecting Network Intrusion[C]//Proc. of International Conference on Advances in Computing, Communication and Control. Murnbai, Maharashtra, India: Is. n.], 2009.
  • 4Dietterich T G. Machine Learning Research: Four Current Directions[J]. AI Magazine, 1997, 18(4): 97-136.
  • 5Oza N, Turner K. Classifier Ensembles Select Real-world Applications[J]. Information Fusion, 2008, 9(1 ): 4-20.
  • 6Gandomkar M, Vakilian M, Ehsan M A Genetic-based Tabu Search Algorithm for Optimal DG Allocation in Distribution Networks[J]. Electricty Power Component System, 2005, 33(12): 1351-1361.
  • 7Jiang Tianzi, Cui Qinghua, Shi Gguihua, et al. Protein Folding Simulations of the Hydrophobic-hydrophilic Model by Combining Tabu Search with Genetic Algorithms[J]. Journal of Chemical Physics, 2003, 119(8): 4592-4596.
  • 8计智伟,吴耿锋,胡珉.基于自适应遗传算法和SVM的特征选择[J].计算机工程,2009,35(14):200-202. 被引量:9
  • 9郑洪英,侯梅菊,王渝.入侵检测中的快速特征选择方法[J].计算机工程,2010,36(6):262-264. 被引量:23
  • 10陈友,沈华伟,李洋,程学旗.一种高效的面向轻量级入侵检测系统的特征选择算法[J].计算机学报,2007,30(8):1398-1408. 被引量:46

二级参考文献33

  • 1唐焕文,张立卫,王雪华.一类约束不可微优化问题的极大熵方法[J].计算数学,1993,15(3):268-275. 被引量:75
  • 2唐焕文,张立卫.凸规划的极大熵方法[J].科学通报,1994,39(8):682-684. 被引量:49
  • 3李兴斯.一类不可微优化问题的有效解法[J].中国科学(A辑),1994,24(4):371-377. 被引量:137
  • 4陈友,程学旗,李洋,戴磊.基于特征选择的轻量级入侵检测系统[J].软件学报,2007,18(7):1639-1651. 被引量:78
  • 5黄卫.加强技术风险管理,确保地铁工程建设安全[C]//2005地铁与地下工程技术风险管理研讨会.北京:[出版者不详],2005.
  • 6赵萤.支持向量机中高斯核函数的研究[D].上海:华东师范大学,2007.
  • 7Wang Yujia, Yang Yupu. Particle Swarm Optimization with Preference Order Ranking for Multi-objective Optimization[J]. Information Sciences, 2009, 179(12): 1944-1959.
  • 8Kiranyaz S, Ince T, Yildirim A, et al. Evolutionary Artificial Neural Networks by Multi-dimensional Particle Swarm Optimization[J]. Neural Networks, 2009, 22(10): 1448-1462.
  • 9Marinakis Y, Marinaki M. A Hybrid Multi-swarm Particle Swarm Optimization Algorithm for the Probabilistic Traveling Salesman Problem[J]. Computers & Operations Research, 2010, 37(3): 432-442.
  • 10Park Jong Sou,Shazzad K M,Kim D S.Toward modeling lightweight intrusion detection system through correlationbased hybrid feature selection//Feng D,Lin D,Yung M eds.Proceedings of the CISC.Heidelberg:Springer-Verlag,2005:279-289

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