Mobile Service Traffic Classification Based on Joint Deep Learning with Attention Mechanism

Abstract

With the rapid development of mobile devices, smartphones have become the chief access to Internet and generated huge mobile service traffic. Mobile service traffic classification (MSTC) has been an important task that contributes to providing personalized services for end-users. With the excellent ability of automatic feature learning, deep learning has better performance than traditional machine learning methods. Giving more attention to a local focus, the attention mechanism can reduce computational complexity by filtering out useless information. Therefore, deep learning with attention mechanism can effectively realize automatic feature learning and reduce computational complexity. In this paper, a novel method for MSTC with a two-step strategy is proposed, which reduces the computational complexity of the deep learning model by attention mechanism. In the first step, a joint deep learning model is designed as a basic classifier, which learns features of mobile service traffic from multiple time scales. In the second step, the attention mechanism is adopted to aggregates the basic predictions generated in the first step. To verify this methodology, an experiment is performed to classify seven mobile services. The results show that we get the mean F1-score of 92.7% with 3.1 seconds time-delay, where the pure deep learning model gets the highest mean F1-score of 90.4% with 6.7 seconds time-delay.