基于非线性投影和带惩罚函数的SVM的入侵检测问题
The SVM Intrusion Detection Problem Based on Nonlinear Projection and Penalty Function

作者: 崔玉泉 , 李琳琳 , 曹丹星 :山东大学数学学院,济南;

关键词: 非线性投影罚函数支持向量机入侵检测Nonlinear Projection Penalty Function Support Vector Machine Intrusion Detection

摘要:
本文根据线性投影寻踪的思想,提出了一种非线性投影方法。该方法通过非线性投影将高维数据降低到低维空间,对于由此投影得到的低维数据再利用带惩罚函数的非线性支持向量模型,实现对入侵数据的检测。最后,利用KDD99数据对本模型进行验证,以说明其有效性。经计算验证,效果较为理想。

Abstract: Based on the idea of a linear projection pursuit, we propose a method for nonlinear projection. The nonlinear projection method will reduce the high dimensional data into low-dimensional space. For low-dimensional data projection thus obtained with a penalty function reuse nonlinear support vector model and implement intrusion detection data. Finally, we use the KDD99 data set to illustrate the model’s effectiveness. Verified by calculation, the effect is more ideal.

文章引用: 崔玉泉 , 李琳琳 , 曹丹星 (2014) 基于非线性投影和带惩罚函数的SVM的入侵检测问题。 数据挖掘, 4, 27-31. doi: 10.12677/HJDM.2014.44004

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