Evaluation of Support Vector Machine Kernels for Detecting Network Anomalies

Border Gateway Protocol (BGP) is used to exchange routing information across the Internet. BGP anomalies severely affect network performance and, hence, algorithms for anomaly detection are important for improving BGP convergence. Efficient and effective anomaly detection mechanisms rely on employing machine learning techniques. Support Vector Machine (SVM) is a widely used machine learning algorithm. In this paper, we evaluate performance of SVM with linear, quadratic, and cubic kernels. The SVM kernels are compared based on accuracy and the F-Score when detecting BGP anomalies in Internet traffic traces. The performance heavily depends on the selected features and their combinations.