The fine-grained classification of encrypted traffic is important for network security analysis. Malicious attacks are usually encrypted and simulated as normal application or content traffic. Supervised machine learning methods are widely used for traffic classification and show good performances. However, they need a large amount of labeled data to train a model, while labeled data is hard to obtain. Aiming at solving this problem, this paper proposes a method to train a model based on the K-nearest neighbor (KNN) algorithm, which only needs a small amount of data. Due to the fact that the importance of different traffic features varies, and traditional KNN does not highlight the importance of different features, this study introduces the concept of feature weight and proposes the weighted feature KNN (WKNN) algorithm. Furthermore, to obtain the optimal feature set and the corresponding feature weight set, a feature selection and feature weight self-adaptive algorithm for WKNN is proposed. In addition, a three-layer classification framework for encrypted network flows is established. Based on the improved KNN and the framework, this study finally presents a method for fine-grained classification of encrypted network flows, which can identify the encryption status, application type and content type of encrypted network flows with high accuracies of 99.3%, 92.4%, and 97.0%, respectively.

Since that collaborative plan, manufacturing plan and scheduling solutions are hard to be paralleled and synchronized, after considering the scheduling with collaborative plan under the circumstance of supply chain, the multi-objective model of integrated collaborative planning and scheduling for dual-resource is established, which contains two resource constraints: the machines and workers. The model achieves three goals: the shortest completion time, the lowest cost and the minimum total tardiness. Then, an improved SPEA2 is proposed for the model. In this algorithm, the chromosome pair encoding which is suitable for the requirements of dual-resource and collaborative decision is designed. It includes process coding, machine coding, worker coding and collaborative decision variate, and the crossover operation and mutation operation of chromosome are improved. Finally, the correctness of model and the efficiency of algorithm are proved by simulation experiment.