Existing malicious encrypted traffic detection approaches need to be trained with many samples to achieve effective detection of a specified class of encrypted traffic data. With the rapid development of encryption technology, various new types of encrypted traffic are emerging and difficult to label. Therefore, it is an urgent problem to train a deep learning model using only a small number of samples to detect new classes of malicious encrypted traffic. This paper proposes a few-shot malicious encrypted traffic detection (FMETD) approach based on model-agnostic meta-learning (MAML), integrating feature selection and classification into an end-to-end framework. The FMETD approach first converts the raw traffic data into two-dimensional grayscale images. Then, FMETD trains a deep learning model (2D-CNN) using the MAML, which is to learn an optimal set of model initialization parameters for the model from a set of classification tasks consisting of grayscale images. The model with this set of parameters can detect new classes of maliciously encrypted traffic data efficiently with a few samples by a few iterations steps. The experimental results show that the FMETD approach has 99.8% accuracy for two-class classification encrypted traffic and 98.5% average accuracy for multi-classification. When the number of grayscale images of each class in the support set and validation set is reduced to 20, the accuracy of our approach to multi-class classification is 97.9% for new classes of traffic.

This paper designs and implements a private cloud platform deployed on an office system that supports domestic software and hardware. With the rapid development of cloud computing, more and more enterprises and users choose cloud platform as a vital Internet resource. At present, most private cloud technologies rely on mature foreign commercial applications and frameworks, and it isn’t easy to achieve compatibility between Chinese software and hardware. Therefore, it is urgent to design a private cloud platform that supports Chinese software and hardware. The key private cloud technology of the cloud platform designed in this paper is the key technology of private cloud that supports independent and controllable Chinese software and hardware. The cloud platform uses virtual computing, virtual storage, virtual network, and other technologies to complete the virtualization of computing resources, storage resources, and network resources. Users can centrally schedule and manage virtual resources.