基于KFDA-SVM的入侵检测技术（增刊）

为了提高分类正确率和减少训练时间，将特征抽取技术与分类算法结合，提出了一种基于KFDA-SVM的入侵检测技术。采用KFDA抽取最佳鉴别矢量，运用SVM对投影后的数据分类。同时根据入侵数据高维异构小样本的特性，提出一种基于HVDM的混和核函数。采用KDD 99数据集进行试验，验证了该算法的有效性。

Intrusion Detection Technology Based on KFDA-SVM

In order to improve the detection rate and reduce the training time, KFDA-SVM intrusion detection technology is proposed which combines the feature extraction technology and classification algorithm. In the proposed algorithm, the KFDA is used to extract the optimal discriminant vectors and then the SVM is adopted to classify the projected data. A mixture of kernels based HVDM is proposed according to the high dimensional and heterogeneous datasets acquired in the intrusion detection. Results of the experiment using KDD 99 indicate the effectiveness of the algorithm.