EETC: An extended encrypted traffic classification algorithm based on variant resnet network

With the advent of big data, encrypted traffic is widely used, and it is gradually becoming a new challenge for network security and management. Many researchers have obtained good results by converting encrypted traffic into images and feeding them to deep learning-based classification models. However, these methods have some limitations in that they cannot do sustainable learning. They have to retrain a new classifier when new traffic is encountered. To tackle this issue, we propose an extended encrypted traffic classification algorithm based on incremental learning. This approach extracts content and statistical information from encrypted traffic and supports multi-label prediction for VPN channels and applications. We can add new classes to the model without completely retraining and accelerate the update cycle of the model. The results show that the proposed model performs well in classification experiments, which can achieve 98.1% and 96.0% classification accuracy on ISCXVPN2016 and self-collected dataset respectively. In addition, our method can retain high accuracy under the situation of limited memory space while the number of new classes of data increases gradually. It demonstrates the superiority of constructing a generic unforgetting basis for classifying encrypted communication.