Incremental Learning for Mobile Encrypted Traffic Classification

With the rising popularity of mobile networks and applications, network traffic classification has gradually become essential to mobile network management and cyberspace security. Existing state-of-the-art methods have achieved high accuracy in the closed-world mobile encrypted traffic classification, where the classifier only needs to process the classes seen in the training. When we update the dataset with new mobile applications, these methods must retrain a new classifier from scratch to learn the knowledge of all applications because directly fine-tuning the existing classifier would lead to the catastrophic forgetting problem. Thus, it is challenging to incrementally add new applications to the classification system while preserving the learned knowledge of the existing classifier. To tackle this issue, we propose an incremental learning framework based on the one vs rest (OvR) strategy and neural network classifiers. Moreover, we adopt a sample selection algorithm to balance the conflict between the growing training effort caused by new applications and the high classification accuracy. The experimental results demonstrate that our proposed framework achieves incremental learning with high classification accuracy like the closed-world method, and the selection algorithm significantly reduces training efforts to meet the dataset scale control and classification accuracy requirement in the lifetime incremental learning.