Title: Anomaly detection model based on support vector machine and Bayesian classification
Abstract:Through the research into the types of network attack and the intrusion detection methods,the fact that the normal intrusion detection method was not good enough for detecting U2R(User to Root) and R2...Through the research into the types of network attack and the intrusion detection methods,the fact that the normal intrusion detection method was not good enough for detecting U2R(User to Root) and R2L(Remote to Local) was found.To improve the detection rate of anomaly detection system for U2R and R2L,an anomaly detection model based on Support Vector Machine(SVM) and Bayesian classification was suggested.In order to reduce the redundant records in the training data,the BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm was used.Besides,the detection model applied SVM for detecting DoS and Probe and used Bayesian classification to detect U2R and R2L.The experimental results show that the proposed model improves obviously the detection rate for U2R and R2L,up to 68.6 percent and 45.7 percent respectively.Read More
Publication Year: 2012
Publication Date: 2012-01-01
Language: en
Type: article
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