摘要：

针对入侵检测系统中传统决策树分类算法仅能处理离散化数据的情况,提出一种改进的入侵检测方法。通过云模型对数据集连续属性进行离散化,利用遗传算法引入加权选择概率函数,使得决策树分类算法能检测出DoS、R2L、U2R、PRB攻击。KDDCUP 99数据集上的实验结果表明,与基于贝叶斯、支持向量机与云模型离散化的检测方法相比,该方法具有更好的入侵检测与分类性能。

关键词: 云模型, 决策树, 离散化, 遗传算法, 入侵检测, 连续属性

Abstract:

Aiming the problem that the traditional decision tree classification algorithm in intrusion detection system can only deal with discrete data,an improved intrusion detection method is proposed.The cloud model is used to discretize the continuous attribute of datasets and the genetic algorithm is used to introduce the weighted selection probability function so that the decision tree classification algorithm can detect the attack of DoS,R2L,U2R and PRB.Experimental result of the KDDCUP 99 dataset shows that this method has better intrusion detection and classification performance compared with detection method based on Bayes,Support Vector Machine(SVM) and cloud model discretization.

Key words: cloud model, decision tree, discretization, genetic algorithm, intrusion detection, continuous attribute

中图分类号: 

• TP391