提出了一种基于支持向量数据描述算法的异常检测方法。

This paper proposes a new anomaly intrusion detection method based on support vector data description (SVDD).

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支持向量域数据描述(SVDD)是一种单值分类算法，用于将目标样本与其他非目标样本区分开来。

As a type of one-class classification algorithm, Support Vector Data Description (SVDD) was used to distinguish target objects from outlier objects.

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该算法根据支持向量域数据描述本身的特点，利用非高斯性来测量核空间样本接近球形区域分布的程度，并根据此测量结果来优化核参数。

According to the characteristic of SVDD, the proposed algorithm utilizes the non-Gaussian to measure how kernel samples approximate to a spherical area, and then optimize the kernel parameter.

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