Internet traffic classification is a process of identifying network applications and classifying corresponding traffic, which is considered as the most basic function of modern network management and security system. And application-related traffic classification is the basic technology of recent network security. Traditional traffic classification methods include port-based prediction methods and payload-based depth detection methods. In current network environment, there are some practical problems in traditional methods, such as dynamic ports and encryption applications. Therefore, Machine Learning (ML) technology based on traffic statistics is used to classify and identify traffic. Machine learning can realize centralized automatic search by using provided traffic data and describe useful structural patterns, which is helpful to intelligently classify traffic. Initially, Naive Bayes method was used to identify and classify network traffic classification, performing well on specific flows with accuracy over 90%, while on traffic such as peer-to-peer transmission network traffic (P2P) with accuracy only about 50%. Then, methods such as Support Vector Machine (SVM) and Neural Network (NN) were used, and neural network method could make accuracy of overall network classification reach 80% or more. A number of studies show that the use of a variety of machine learning methods and their improvements can improve the accuracy of traffic classification.

Multi-constraint constrains and reduces the test success rate, and it is difficult to make the knowledge points uniformly and automatically distributed in intelligent generating test paper. To solve these above problems, a multi-constraint hierarchical optimization strategy was proposed. It used hierarchical method to reduce the problem size, and used the tree structure to manage knowledge points and realize uniform distribution of knowledge points. With regard to the low success rate and efficiency of small test bank in generating test paper, a forecast calculation algorithm without backtracking was put forward based on hierarchical optimization algorithm to increase the test success rate. The experimental results indicate that the algorithm is suitable for large, medium and small question database, and all of them have good results.