Intrusion Detection System Based on Support Vector Machine and Adaboost
摘要
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.