Subspace clustering via graph auto-encoder network for unknown encrypted traffic recognition

Abstract

The traffic encryption brings new challenges to the identification of unknown encrypted traffic. Currently, machine learning is the most commonly used encrypted traffic recognization technology, but this method relies on expensive prior label information. Therefore, we propose a subspace clustering via graph auto-encoder network (SCGAE) to recognize unknown applications without prior label information. The SCGAE adopts a graph encoder-decoder structure, which can comprehensively utilize the feature and structure information to extract discriminative embedding representation. Additionally, the self-supervised module is introduced, which use the clustering labels acts as a supervisor to guide the learning of the graph encoder-decoder module. Finally, we obtain the self-expression coefficient matrix through the self-expression module and map it to the subspace for clustering. The results show that SCGAE has better performance than all benchmark models in unknown encrypted traffic recognization.