DoH traffic classification based on knowledge distillation
To address the challenges faced by traditional deep learning methods in DoH traffic classification,such as dependence on large amounts of annotated data,high risk of overfitting,and poor model interpretability,a DoH traffic classification method based on knowledge distillation was proposed.Firstly,a convolutional neural network(CNN)with two convolutional layers and two fully connected layers was designed for student model and teacher model training.Secondly,the student model and the teacher model were initialized to make the teacher model a deep copy of the student model with fixed parameters.Finally,the training was performed by the weighted sum of classification loss and consistency loss,and the teacher model parameters were updated using exponential moving average(EMA)to provide more stable guidance.The experimental results on CIRA-CIC-DoHBrw-2020 dataset show that,compared with the traditional 1D-CNN model,this method improves precision,recall and Fl score by 0.13,0.63,and 0.40 percentage points,demonstrating the effectiveness of the knowledge distillation in improving model performance.
knowledge distillationDoH traffic classificationconvolutional neural network(CNN)mean teacher model
万方数据
维普
