摘要： 入侵检测系统在大数据量的情况下误报率高、泛化能力弱，且单一机器学习算法不能较好地应对多种攻击类型。为此，设计一个基于支持向量机（SVM）与Adaboost算法的入侵检测系统。依托Snort系统，利用主成分分析方法对提取的特征做降维处理，并将SVM-Adaboost集合算法作为检测引擎。采用NSL-KDD数据集进行训练和测试，实验结果表明，该系统的正确率达到97.3%，较SVM算法和Adaboost算法分别提高4.8%和14.3%。

关键词: 支持向量机, Adaboost算法, 数据降维, 入侵检测系统, 接受者操作特征曲线

Abstract: The Intrusion Detection System(IDS) has high false alarm rate and weak generalization ability in the case of large amount of data,and the single machine learning algorithm can not cope with multiple attack types well.To address this problem,this paper designs an IDS based on Support Vector Machine(SVM) and Adaboost algorithm.It relies on Snort system,which uses Principal Component Analysis(PCA) method to reduce the dimension of extracted features and uses the SVM-Adaboost clustering algorithm as detection engine.NSL-KDD dataset is used for training and testing.Experimental results show that the accuracy of the proposed system reaches 97.3%,which is improved by 4.8% and 14.3% respectively compared with the SVM algorithm and Adaboost algorithm.

Key words: Support Vector Machine(SVM), Adaboost algorithm, data dimension reduction, Intrusion Detection System(IDS), Receiver Operating Characteristic(ROC) curve

中图分类号: 

• TP309