Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identification has been a research hotspot in the field of network management and security mon-itoring.However,as more network services use encryption technology,network traffic identification faces many challenges.Although classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and labor-intensive.Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples.However,in real scenarios,labeled samples are often difficult to obtain.This paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning.Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

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

The traffic encryption brings new challenges to the identification of unknown encrypted traffc.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.