Accurate Classification of the Internet Traffic Based on the SVM Method

The need to quickly and accurately classify Internet traffic for security and QoS control has been increasing significantly with the growing Internet traffic and applications over the past decade. Pattern recognition by learning the features in the training samples to classify the unknown flows is one of the main methods. However, many methods developed in the previous works are too complicated to be applied in real-time, and the prior probabilities based on the training samples are severely biased. This paper uses the SVM (support vector machine) method to train 7 classes of applications of different characteristics, captured from a campus network backbone. A discriminator selection algorithm is developed to obtain the best combination of the features for classification. Our optimized method yields approximately 96.9% accuracy for un-biased training and testing samples. For regular biased samples, the accuracy is about 99.4%. Furthermore, all the feature parameters are computable in real time from captured packet headers, suggesting real time network traffic classification with high accuracy is achievable.