计算机应用与软件2018，Vol.35Issue(3)：140-144,5.DOI:10.3969/j.issn.1000-386x.2018.03.027
一种基于核PCA的网络流量异常检测算法
A NETWORK ANOMALY DETECTION ALGORITHM BASED ON KERNEL PCA
曾建华1
作者信息
- 1. 上饶师范学院数学与计算机科学学院 江西上饶334001
- 折叠
摘要
Abstract
Anomaly detection of various network data traffic has aroused people's interest.Network data flow anomaly detection and positioning of the network for the safe and secure operation is extremely important.Although PCA-based network anomaly detection algorithm has good detection performance, the premise of PCA-based algorithm is that it assumes that the network data satisfies the Gauss distribution and it cannot describe the nonlinear capability of network data.To address this problem,a new algorithm for online traffic anomaly detection based on kernel principal component analysis was proposed by taking advantages of kernel function space.In this work,the normal subspace and the abnormal subspace were constructed by matrix decomposition,and the detection of the network traffic anomaly was implemented. Simulation results showed that the proposed algorithm had good performance.关键词
网络异常检测/核主成分分析/核函数/优化Key words
Network anomaly detection/Kernel PCA/Kernel function/Optimization分类
信息技术与安全科学引用本文复制引用
曾建华..一种基于核PCA的网络流量异常检测算法[J].计算机应用与软件,2018,35(3):140-144,5.
