Online Traffic Classification Based on Co-training Method

Abstract

Online traffic classification has been widely used in quality of service measurements, network management and security monitoring. Currently, more and more research works tend to apply machine learning techniques to online traffic classification, and most of them are based on supervised learning and unsupervised learning techniques. Although supervised learning method has exhibited good classification performance, it needs lots of labeled training samples which are difficult to collect. The co-training method is a semi-supervised learning method, which can use little labeled samples and plenty of unlabeled samples to enhance the performance of supervised learning method. In this paper, we investigate the co-training algorithm for online traffic classification. The co-training algorithm needs two separate features which are sufficient to train a good classifier. We choose packet size and inter-packet time of the first packets of a traffic flow as two features. However, the inter-packet time is dependent to network conditions and will be impacted by network jitter. This paper constructs a robust inter-packet time feature named "Netipt" which is resilient to network jitter, and we integrate Netipt feature to co-training algorithm. We test our co-training algorithm based on two real-world traffic datasets. The results show that the co-training algorithm can enhance the accuracy of traffic classification drastically even when there are very few training samples.