基于信息熵的SVM入侵检测技术

摘要

在传统基于SVM的入侵检测中,核函数构造和特征选择采用先验知识,普遍存在准确度不高、效率低下的问题.通过信息熵理论与SVM算法相结合的方法改进为基于信息熵的SVM入侵检测算法,可以提高入侵检测的准确性,提升入侵检测的效率.基于信息熵的SVM入侵检测算法包括两个方面:一方面,根据样本包含的用户信息熵和方差,将样本特征统一,以特征是否属于置信区间来度量.将得到的样本特征置信向量作为SVM核函数的构造参数,既可保证训练样本集与最优分类面之间的对应关系,又可得到入侵检测需要的最大分类间隔；另一方面,将样本包含的用户信息量作为度量大幅度约简样本特征子集,不但降低了样本计算规模,而且提高了分类器的训练速度.实验表明,该算法在入侵检测系统中的应用优于传统的SVM算法.%In traditional SVM based intrusion detection approaches,both core function construction and feature selection use prior knowdege.Due to this,they are not only inefficient but also inaccurate.It is observed that integrating information entropy theory into SVM-based intrusion detection can enhance both the precision and the speed.Concludely speaking,SVM-based entropy intrusion detection algorithms are made up of two aspects:on one hand,setting sample confidence vector as core function's constructor of SVM algorithm can guarantee the mapping relationship between training sample and optimization classification plane.Also,the intrusion detection's maximum interval can be acquired.On the other hand,simplifying feature subset with samples's entropy as metric standard can not only shrink the computing scale but also improve the speed.Experiments prove that the SVM based entropy intrusion detection algoritm outperfomrs other tradional algorithms.