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基于二维熵分量的K均值攻击效果评估
引用本文:戴方芳 郑康锋 胡 影 李忠献. 基于二维熵分量的K均值攻击效果评估[J]. 北京邮电大学学报, 2014, 37(1): 71-75. DOI: 10.13190/j.jbupt.2014.01.016
作者姓名:戴方芳 郑康锋 胡 影 李忠献
作者单位:1. 北京邮电大学 信息安全中心, 北京 100876;
2. 武警北京指挥学院, 北京 100012;
3. 国瑞数码安全系统有限公司, 北京 100088
基金项目:国家自然科学基金项目(61070204,61101108);中央高校基本科研业务费专项资金项目(BUPT2012PTB0102);国家科技支撑计划项目(2012BAH37B05)
摘    要:提出了一种利用二维熵分量的K均值攻击效果聚类评估方法. 利用网络熵预处理攻击数据集,将效果数据映射到二维平面,并以二维熵分量作为聚类的输入,然后基于K均值算法建立聚类数据集与效果分类之间的关系,实现了对网络攻击效果结果集的明确划分,并提供快速有效的评估结果. 仿真实验结果证明,该方法能高效正确地处理攻击数据,并以评估结果类图的形式提供直观的评估结果.

关 键 词:效果评估  聚类    K均值  
收稿时间:2013-04-12

A K-Means Cluster Evaluation of Attack Effect Based on Bi-Dimensional Entropy Components
DAI Fang-fang,ZHENG Kang-feng,HU Ying,LI Zhong-xian. A K-Means Cluster Evaluation of Attack Effect Based on Bi-Dimensional Entropy Components[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(1): 71-75. DOI: 10.13190/j.jbupt.2014.01.016
Authors:DAI Fang-fang  ZHENG Kang-feng  HU Ying  LI Zhong-xian
Affiliation:1. Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Armed Police Beijing Command Academy, Beijing 100012, China;
3. National Cybernet Security Ltd, Beijing 100088, China
Abstract:A K-means cluster evaluation technique using bi-dimensional entropy components was proposed. The attack dataset on the basis of network entropy was preprocessed, a two-dimensional plane was mapped. The output of preprocess as the input of clustering was utilized. And a relation between the attack dataset and the effect category on the basis of K-means algorithm was established, thus an explicit division of attack effect set was achieved. Efficient evaluation was given. Experiment shows that the method can process attack dataset with high efficiency, as well as provide a visualized evaluation result by form of evaluation cluster diagram.
Keywords:effect evaluation  clustering  entropy  K-means algorithm  
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