SAE-based Encrypted Traffic Identification Method
摘要
To solve the problem that encrypted traffic identification methods based on machine learning are low in accuracy and time-consuming and costly in feature extraction and selection,this paper proposes a Stacked Autoencoder (SAE)-based encrypted traffic identification method.The method utilizes the unsupervised characteristics of SAE and its advantages in dimensional reduction,combined with supervised classification learning of Multilayer Perceptron (MLP) to achieve accurate identification of encrypted application traffic.Considering the influence of the class imbalance of the sample dataset on the classifier performance,the unbalanced dataset is processed by the SMOTE oversampling method.Experimental results show that,the performance indicators of this method are higher than the MLP encrypted traffic identification method,and the precision,recall,and F1-Score can reach 99%.