网络入侵特征优化检测方法仿真

Network Intrusion Feature Optimization Simulation Test Method

网络入侵特征的伪装程度越来越高,使得入侵特征与正常数据特征在分类效果上的倾斜度越来越接近。传统的采用特征分类的入侵检测方法对训练入侵特征数据集的最佳类分布未知,都是假定误分类代价均等,只注重分类的精度敏感,忽视了类型间的区别,造成入侵检测不准。提出了一种敏感性数据挖掘的网络入侵特征检测算法。利用主成分分析方法,提取网络操作数据中的主成分,去除冗余数据,将网络入侵特征的敏感性引入到检测过程中,利用敏感性数据挖掘方法,获取网络操作数据中的恶意入侵操作行为的特征,从而完成网络入侵特征检测。实验结果表明,利用改进算法进行网络入侵特征优化检测,能够准确获取网络操作行为中的异常特征。

Research the accurate detection of network intrusion feature.The camouflage degree of network intrusion characteristic is higher and higher,which leads to intrusion detection inaccurate.The paper proposed a sensitivity data mining network intrusion detection algorithm.A principal component analysis method was used to extract the main components in the network operation data,and redundant data were removed.The sensitivity data mining method was used to obtain network operation data from malicious invasion operation behavior characteristics and to complete the characteristics of network intrusion detection.The simulation results show that the improved algorithm can accurately obtain anomaly characteristics of the network operation behavior.