计算机工程与应用Issue(10)：96-100,5.DOI:10.3778/j.issn.1002-8331.1311-0164
基于邻域粗糙集的入侵检测集成算法
Intrusion detection ensemble algorithm based on neighborhood rough set
摘要
Abstract
The intrusion detection data has high dimensionality and nonlinear characteristics, and contains large redundant and noisy attributes, as well as some continuous attributes, this paper presents an ensemble algorithm based on neighborhood rough set to improve the effect of intrusion detection. Many training subsets are generated by Bagging technology, reduced training subsets with large difference are gained using neighborhood rough set with different radius in the training subset, many base classifiers are trained in reduced training subsets, and are ensembled using weighted average method. The exper-imental results in the KDD99 dataset show that the algorithm can effectively improve the accuracy and efficiency of intru-sion detection, it has high generalization and stability.关键词
入侵检测/Bagging技术/邻域粗糙集/支持向量机/集成学习Key words
intrusion detection/Bagging/neighborhood rough set/support vector machine/ensemble learning分类
信息技术与安全科学引用本文复制引用
魏峻..基于邻域粗糙集的入侵检测集成算法[J].计算机工程与应用,2014,(10):96-100,5.基金项目
国家自然科学基金（No.81160183，No.11305097）；陕西理工学院科研基金（No.SLGKY13-41，No.SLGKY12-01）。 （）
