Concerning the problem that the current research on password semantics is mainly based on English datasets and restricted to some units like common words or surnames, by using data analysis technology based on password strings, a Chinese context password analysis method based on known-password elements was proposed with the pattern library based on Chinese poems and idioms in Chinese context. Firstly, the known-password element was identified. Then, it was considered as a single password degree of freedom. Finally, the freedom attack cost within a given attack success rate was calculated and the quantitative security of password was obtained. After quantitative analysis of large amounts of plaintext passwords by designed experiments, it is concluded that 80% of user passwords are low secure and can be easily broken by dictionary attacks in Chinese context.

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.