Early Stage Internet Traffic Identification Using Probabilistic Neural Networks

Abstract

Identifying network traffics at their early stages accurately is very important for network management and security. Recent years, more and more studies have devoted to find effective machine learning models to identify traffics with the few packets at the early stage. In this paper, we try to build an effective early stage traffic identification model by applying probabilistic neural networks. Three network traffic data sets including two open data sets are used for the study. Packet sizes and three statistics are applied as features. Six classical classifiers are employed as the comparing methods in the identification experiments. PNN outperforms the other methods for most cases in the identification experiments, and it behaves very well for both of accuracy and AUC. Thus, PNN is effective for early stage traffic identification.