A Classification Method for Encrypted Traffic Based on Few-shot Learning
Encryption traffic classification is the process of identifying the service,applications,and protocols running behind network encryption traffic in order to improve the quality of network service or provide the security as-surance of networks.Mainstream encryption traffic classification schemes are conducted to train and achieve reliable performance by large datasets.However,with the development of Internet technology,network traffic,calculation nodes,and network services,there are the requirements of different encryption traffic allocations,it becomes more and more impractical to collect and label enough encryption traffic.Therefore,it is crucial to study a technique that can accurately classify encrypted traffic with fewer encryption traffic samples and quickly generalize the model.In this paper,a novel method for encrypted traffic classification based on few-shot learning is proposed.This method simu-lates and optimizes the traffic classification task based on the principles of meta-learning.Moreover,the pre-trained convolutional neural network(CNN)model is used to extract the feature,a novel parameter decomposition method is introduced on the basis of the special computational architecture of CNN to rapidly adapt to the data distribution on various tasks.Finally,through the comparative experiments with N-way and K-shot setting,the experimental results show that the accuracy of the proposed method achieves by 98％with the K coefficient of 10,the accuracy of the few-shot learning method is higher than that of the reference model.
NETL
NSTL
万方数据
维普
