基于样本密度的SVM及其在入侵检测中的应用

SVM algorithm based on sample density and its application in network intrusion detection

针对网络数据集过于庞大,学习速度过慢的问题,提出了一种基于空间块和样本密度的SVM算法,并将其应用到入侵检测中。该算法根据样本的局部密度选择训练样本,减少参加训练的样本数量,提高学习速度。实验结果表明,该算法在保证检测精度的同时,学习速度快于传统SVM入侵检测方法。

When the network dataset is very large, conventional Support Vector Machine （SVM） learning algorithm is remarkably slow. By contrast, the proposed algorithm based on space block and sample density is fast. It was applied in intrusion detection in this paper. The algorithm selects training samples by local sample density, to reduce the training samples and thus to improve the speed of learning, Simulation shows that the algorithm is faster than the techniques of intrusion detection based on conventional SVM while it guarantees the high classification precision.