The sensing of network security situation (NSS) has become a hot issue. This paper first describes the basic principle of Markov model and then the necessary and sufficient conditions for the application of Markov game model. And finally, taking fuzzy comprehensive evaluation model as the theoretical basis, this paper analyzes the application fields of the sensing method of NSS with Markov game model from the aspects of network randomness, non-cooperative and dynamic evolution. Evaluation results show that the sensing method of NSS with Markov game model is best for financial field, followed by educational field. In addition, the model can also be used in the applicability evaluation of the sensing methods of different industries’ network security situation. Certainly, in different categories, and under the premise of different sensing methods of network security situation, the proportions of various influencing factors are different, and once the proportion is unreasonable, it will cause false calculation process and thus affect the results.

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Traditional data mining often focuses on the research of models and methods without considering of specific requirements in the field. This paper proposes a data mining framework in traffic field—Traffic Domain Data-Mining Framework (TDDMF) which is a domain-driven data mining framework based on ontology. The ontology model for traffic domain data based on TDDMF is also built in the paper. A prototype system is developed to prove the availability and effectiveness of TDDMF.