基于主成分分析和决策树的入侵检测方法

特征选择算法能够更好地提高入侵检测系统的检测速度和检测效果,消除冗余数据并减轻噪音特征.结合特征选择算法的优势,提出一种基于主成分分析(PCA)与决策树(C4.5)的入侵检测方法,进而构建出轻量级的入侵检测系统.通过在KDD1999数据集上对该方法进行详细的实验验证,证明该方法一方面确保系统有较高的检测率与较低误报率,另一方面能够比较显著地提高系统的训练时间与测试时间.同时,通过比较实验发现此方法在训练时间、测试时间、检测率、误报率上的效果也优于GA-SVM方法.

An Intrusion Detection Method Based on Principal Component Analysis and Decision Tree

A feature selection algorithm can improve efficiently the detection speed and result, with irrelevant and redundant data eliminated and denoised in an intrusion detection system. Taking advantage of the algorithm, a new hybrid feature selection algorithm based on the principal component analysis (PCA) in combination with decision tree algorithm (C4.5) was proposed to develop a lightweight intrusion detection system. Verifying the proposed algorithm in detail via tests with the KDD 1999 dataset, the algorithm was proved that it is available to not only ensure the high detection rate and low false alarm rate but also improve obviously the training/testing time of the intrusion detection system. Furthermore, as a result of comparative tests, the algorithm is superior to GA-SVM algorithm in training/testing time, detection rate and false alarm rate.