Segmented Learning for Class-of-Service Network Traffic Classification

Class-of-service (CoS) network traffic classification (NTC) classifies a group of similar traffic applications. The CoS classification is advantageous in resource scheduling for Internet service providers and avoids the necessity of remodelling. Our goal is to find a robust, lightweight, and fast-converging CoS classifier that uses fewer data in modelling and does not require specialized tools in feature extraction. The commonality of statistical features among the network flow segments motivates us to propose novel segmented learning that includes essential vector representation and a simple-segment method of classification. We represent the segmented traffic in the vector form using the EVR. Then, the segmented traffic is modelled for classification using random forest. Our solution's success relies on finding the optimal segment size and a minimum number of segments required in modelling. The solution is validated on multiple datasets for various CoS services, including virtual reality (VR). Significant findings of the research work are i) Synchronous services that require acknowledgment and request to continue communication are classified with 99% accuracy, ii) Initial 1,000 packets in any session are good enough to model a CoS traffic for promising results, and we therefore can quickly deploy a CoS classifier, and iii) Test results remain consistent even when trained on one dataset and tested on a different dataset. In summary, our solution is the first to propose segmentation learning NTC that uses fewer features to classify most CoS traffic with an accuracy of 99%. The implementation of our solution is available on GitHub.Comment: The paper is accepted to be appeared in IEEE GLOBECOM 202

Explaining Class-of-service Oriented Network Traffic Classification with Superfeatures

Machine learning has gained wide attention in the networking community for application-based traffic classification. However, identifying applications might not be interesting to the service provider being more concerned about class-of-service (CoS) of the incoming flow. In this paper, we consider CoS-oriented classification using delay sensitivity as an example. We show that the direct approach of predicting CoS labels without inferring application types first is significantly more accurate than the two-step approach where the CoS label is given by a predetermined mapping from application labels. To gain insights about the output predictions of the classifier, we propose an explanation framework based on groups of features called superfeatures and the concept of Shapley values from cooperative games. We implement three learning models namely neural network (NN), support vector machine (SVM), and logistic regression (LREG). Our experimental results show that our explanations are quite consistent over different samples and different approaches of classification.M.A.S