A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering

Abstract

New vulnerabilities and ever-evolving network attacks pose great threats to today's cyberspace security. Anomaly detection in network traffic is a promising and effective technique to enhance network security. In addition to traditional statistical analysis and rule-based detection techniques, machine learning models are introduced for intelligent detection of abnormal traffic data. In this paper, a novel model named SVM-C is proposed for the anomaly detection in network traffic. The URLs in the network traffic log are transformed into feature vectors via statistical laws and linear projection. The obtained feature vectors are fed into a support vector machine (SVM) classifier and classified as normal or abnormal. Based on the idea of SVM and clustering, we construct an optimization model to train the parameters of the feature extraction method and traffic classifier. Numerical tests indicate that the proposed model outperforms the state of the arts on all the tested datasets.