基于粗糙集数据挖掘和分类集成学习的网络入侵检测模型

MODELING NETWORK INTRUSION DETECTION USING ROUGH SET DATA MINING AND ENSEMBLE LEARNING

基于多个特征或多个模型的集成(Ensemb le)学习技术是智能网络入侵检测的重要研究方向,在现有研究基础上提出基于粗糙集分类、模型分发和攻击归类检测,并加以集成的学习式网络入侵检测模型,该模型不仅能提高网络入侵检测系统检测率,同时还结合了粗糙集能处理不确定信息、生成规则具有高解释性、特征排序在获得检测规则前完成等优点。

Ensemble Learning technology is one of the important research directions in intelligent network intrusion detections. Based on the others＇ research in this domain,a new Network Intrusion Detection Modeling technology using Rough Set Data Mining, model distribution and Ensemble Learning is proposed ,which can improve the detection rate and has the advantages of Rough Set Data Mining such as high model explanation,feature ranking and imprecise information adaption etc.