支持向量机在入侵检测系统中的应用  

Application of Support Vector Machine in the Intrusion Detection System

作　　者：王涛[1] 宫会丽[2] 

机构地区：[1]中国海洋大学 [2]中国海洋大学信息工程中心

出　　处：《微计算机信息》2006年第12X期89-91,共3页Control & Automation

摘　　要：为了提高信息系统的安全性,本文将基于统计学习理论的支持向量机方法应用到入侵检测系统中,保证了在先验知识不足的情况下,支持向量机分类器仍有较好的分类正确率,达到了能够对系统异常情况准确预测的目的。该方法避免了基于传统机器学习的局限性,保证了较强的推广能力,从而使整个入侵检测系统具有较好的检测性能。In order to improve the safety of information system, the method of SVM based on statistics learning theory is used in the intrusion detection system, which ensures that SVM classifier has the higher accuracy in the absence of transcendent knowledge, and achieve the aim that SVM can accurately predict the abnormal state of system. By the use of this method, the limitation of traditional machine learning method is avoided and ensure the stronger extension ability.,which make intrusion detection system to have the better detecting performance.

关 键 词：入侵检测 支持向量机 机器学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]