Application of machine learning in BGP anomaly detection

Abstract

Aiming at the problem of BGP anomalies, a support vector machine-based BGP anomaly detection method (SVM-BGPAD) is proposed. We first employ feature selection algorithm based on Fisher linear analysis and Markov random field technology to select features that can maximize the distance among classes and minimize the distance within the class, and then use grid search and cross-validation methods to optimize the parameters of SVM model. We evaluate the performance of classification model with linear, polynomial, RBF and Sigmoid kernels. The results are compared based on accuracy and F1-Score. Experimental results show that the model based on RBF kernel function can achieve the best classification accuracy of 91.36% and F1-Score of 96.03%.