Common Knowledge Based and One-Shot Learning Enabled Multi-Task Traffic Classification

Deep neural networks have been used for traffic classifications and promising results have been obtained. However, most of the previous work confined to one specific task of the classification, where restricts the classifier potential performance and application areas. The traffic flow can be labeled from a different perspective which might help to improve the accuracy of classifier by exploring more meaningful latent features. In addition, deep neural network (DNN)-based model is hard to adapt the changes in new classification demand, because of training such a new model costing not only many computing resources but also lots of labeled data. For this purpose, we proposed a multi-output DNN model simultaneously learning multi-task traffic classifications. In this model, the common knowledge of traffic is exploited by the synergy among the tasks and improves the performance of each task separately. Also, it is showed that this structure shares the potential of meeting new demands in the future and meanwhile being able to achieve the classification with advanced speed and fair accuracy. One-shot learning, which refers to the learning process with scarce data, is also explored and our approach shows notable performance.