摘要
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.
出处
《计算机应用》
CSCD
北大核心
2007年第4期838-840,共3页
journal of Computer Applications
基金
国家自然科学基金资助项目(50279041)
关键词
入侵检测
支持向量机
空间块
样本密度
边缘向量
intrusion detection
Support Vector Machine (SVM)
space block
sample density
marginal vectors