网络入侵检测中粗糙集与支持向量机的混合方法

L. Zhiguo, Kang Jincui, Li Yuan
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引用次数: 6

摘要

为了避免网络入侵,研究和开发了网络入侵检测方法。本文采用粗糙集和支持向量机的混合方法进行网络入侵检测。该检测模型包括粗糙集数据约简和支持向量机网络入侵识别。通过收集680个案例来研究本文所提出方法的优越性,其中460个案例作为训练数据,220个案例作为测试数据。采用普通支持向量机与粗糙集支持向量机的混合方法进行比较。实验结果表明,粗糙集支持向量机检测方法的检测精度优于支持向量机检测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid method of rough set and support vector machine in network intrusion detection
In order to avoid the network intrusion, the network intrusion detection method is studied and developed. In the paper, a hybrid method of rough set and support vector machine are adopted to network intrusion detection. The detection model includes the data reduction by rough set and network intrusion recognition by support vector machine. The 680 cases are collected to study the superiority of the proposed in the paper, where 460 cases are applied as the training data and 220 cases are applied as the testing data. Normal support vector machine is adopted to compare with the hybrid method of rough set-support vector machine. The experimental results show that the detection accuracy of rough set-support vector machine detection method than that of support vector machine.
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