采用序列特征与知识引导的未知网络应用早期识别方法

To address the problem that the existing network traffic classification methods arc not able to promptly identify early unknown applications in case of data scarcity, a method for the early identification of unknown applications based on sequence features and knowledge guidance is proposed in this paper. On the one hand, a traffic classification model, named FAIN, is constructed using the Transformer-encoder framework. By leveraging the self-attention mechanism, FAIN model effectively captures global dependencies among packets and generates distinguishable representation for classification. On the other hand, to improve the adaptability of FAIN model to data scarcity, a model optimization strategy that couples supervised pre-training with meta-learning is proposed. This strategy empowers the model to quickly learn unseen tasks in a few-shot setting.In this study, in-depth comparative experiments arc conducted on few-shot datasets synthesized from real campus network traffic. The results show that the proposed FAIN traffic classification model is superior to existing methods in terms of public network traffic classification. The optimized FAIN model improves the accuracy on the 5-class and 10-class few-shot classification tasks of the XJTU-FSTC and CSTNET datasets, with the maximum accuracy increases of 16. 75%, 10. 08% and 11. 57%, 8. 24%, respectively. This model provides effective support for the early identification of unknown applications.