| 基于均值聚类分析和多层核心集凝聚算法相融合的网络入侵检测 |
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| 引用本文： | 石云,陈钟,孙兵. 基于均值聚类分析和多层核心集凝聚算法相融合的网络入侵检测[J]. 计算机应用研究, 2016, 33(2) |
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| 作者姓名： | 石云 陈钟 孙兵 |
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| 作者单位： | 六盘水师范学院 计算机科学与信息技术系,北京大学 信息科学技术学院,北京大学 信息科学技术学院 |
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| 基金项目： | 国家自然科学基金(61170263)“无线射频识别系统中继攻击的抵御机制研究”；广东省高等教育学会实验室管理专业委员会(GDJ2012063) |
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| 摘 要： | 为了提高网络入侵的检测率,以降低误检率,提出一种基于均值聚分析和多层核心集凝聚算法相融合的网络入侵检的网络入侵检测模型。利用K-Means算法对多层核心集凝聚算法的核心集,用其替代原粗化过程得到的顶层核心集,实现了顶层核心集的快速准确定位,简化了算法的计算复杂性。然后,将KM-MulCA算法应用到入侵检测模型,最后采用KDD CUP 99数据集进行仿真实验。结果表明,本模型可以获得理想的网络入侵检测率和误检率。
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| 关 键 词： | 网络入侵检测 多层凝聚算法 K均值聚类算法 支持向量机 |
| 收稿时间： | 2014-10-22 |
| 修稿时间： | 2014-11-30 |
Intrusion Detection Model Based On Improved Multilayer Condensation Algorithm |
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| SHI Yun,CHEN Zhong and Sun Bing. Intrusion Detection Model Based On Improved Multilayer Condensation Algorithm[J]. Application Research of Computers, 2016, 33(2) |
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| Authors: | SHI Yun CHEN Zhong Sun Bing |
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| Affiliation: | Liupanshui Normal College,Department of Computer Science and Information Technology,Guizhou,Liupanshui,School of Electronics Engineering and Computer Science,Peking University,Beijing,Haidianqu,School of Electronics Engineering and Computer Science,Peking University,Beijing,Haidianqu |
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| Abstract: | In order to improve the detection rate of intrusion detection model and reduce the false negative rate and error detection rate, a novel network intrusion detection model is proposed based on K-Means and Multilayer condensation algorithm. Firstly, K-Means algorithm is used to obtain the core algorithm of MulCA set selection process, substitute for the top core raw coarsening process has been set, realized the fast and accurate positioning of the core set, and may be appropriate to reduce the aggregation layer, simplifies the computation complexity of the algorithm, and then, the proposed algorithm is applied to the intrusion detection model, the experimental results show that the proposed algorithm can obtain good intrusion results. |
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| Keywords: | network intrusion detection multilayer condensation algorithm k-means support vector machine |
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