Abstract: Deep learning algorithm is widely used in network traffic classification,which has good classification effect.Convolutional neural network can not only greatly improve the accuracy of network traffic classification,but also simplify the classification process.However,neural network is faced with security threats such as adversarial attack.The impact of these security threats on network traffic classification based on neural network needs to be further researched and verified.This paper proposes an adversarial attack method for network traffic classification based on convolutional neural network.By adding the disturbance which is difficult to recognize by human eyes to the deep learning input image converted from network traffic,it makes convolutional neural network misclassify network traffic.At the same time,to this attack method,this paper also proposes a defense method based on mixed adversarial training,which combines the adversarial traffic samples generated by adversarial attack and the original traffic samples to enhance the robustness of the classification model.We evaluate the proposed method on public data sets.The experimental results show that the proposed adversarial attack method can cause a sharply drop in the accuracy of the network traffic classification method based on convolutional neural network,and the proposed mixed adversarial attack training can effectively resist the adversarial attack,so as to improve the robustness of the network traffic classification model.

Key words: Adversarial attack, Adversarial training, Deep learning, Machine learning, Traffic classification