Abstract: In this paper, a network intrusion detection method based on PSVMs is constructed. Multiple PSVMs can run at distributed computer system environment. Using feedback to update the initial classifiers, avoid the problem that the learning performance is subject to the distribution state of the data samples in different subsets. Comparison of detection ability between the above detection method and BP neural network, The experiments show that this method can achieve high detection efficiency and low false positive efficiency. And it is superior to traditional BP network method in training time, and has better general ability.

Key words: Statistical Learning Theory, PSVMs, Network Security, Intrusion Detection

摘要： 本文构造了一种基于并行支持向量机（Parallel Support Vector Machines ,简称PSVMs）的网络入侵检测(Intrusion Detection，ID)方法，多个并行的支持向量机在分布式的计算机系统环境上运行。利用反馈对初始的分类器进行更新，避免了初始训练样本的分布差异过大而对分类器性能产生的潜在影响。将其与神经网络检测模型进行对比，实验证明，该方法在保持较低误警率的同时有着很好的检测率，在训练时间上优于传统BP网络方法，并且能保证较好的泛化能力。

关键词: 统计学习理论, 并行支持向量机, 网络安全, 入侵检测