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A New Data-Based Method for Intrusion Feature Selection

机译:一种用于入侵功能选择的新基于数据方法

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The data processed by the intrusion detection system contain large numbers of redundancy and noise features,which result in long training time and bad performance of the detecting ability.This paper gives a new data-based method for the SVM feature selection,which embeds the feature selection to the same studying framework of SVM directly,and onlyselects the features by the given training samples.It is also proved that the algorithm is effective in the datasel of KDD CUP 99.
机译:由入侵检测系统处理的数据包含大量的冗余和噪声功能,这导致了培训时间长,检测能力的差效不良。本文给出了一个新的基于数据的SVM特征选择方法,它嵌入了该功能直接选择SVM的相同研究框架,并且仅通过给定的训练样本选择特征。还证明该算法在KDD杯99的数据区中有效。

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