A New Network Traffic Identification Base on Deep Factorization Machine

Abstract

Effective network traffic identification has important significance for network monitoring and management, network planning and user behavior analysis. In order to select and extract the most effective attribute as well as explore the inherent correlation between the attributes of network traffic. We proposed a new network traffic identification method based on deep factorization machine (DeepFM) which can classify and do correlation analysis simultaneously. Specifically, we first embed the feature vector into a joint space using a low-rank matrix, then followed by a factorization machine (FM) which handle the low-order feature crosses and a neural network which can learn the high- order feature crosses, finally the low-order feature crosses and high-order feature crosses are fused and give the classified result. We validate our method on Moore dataset which is widely used in network traffic research. Our results demonstrate that DeepFM model not only have a strong ability of network traffic identification but also can reveal some inherent correlation between the attributes.