Research On A Quantification Method Of Network Security Situational Awareness Based On Data Fusion

The proposal of network security situational awareness (NSSA) research means a breakthrough and an innovation to the traditional network security technologies, and it has become a new hot research topic in network security field. A new perspective to observe the network security status is demanded in NSSA, timely detection of potential or arisen anomalies in network system is emphasized, and also forecast of network security status in some time later should be made reasonably and exactly. The main idea of NSSA is to make full use of one or several kinds of mathematical methods to fuse the multi-source heterogeneous data in the network system, and then easily understandable network security situation will be generated from a cognitive perspective.Firstly, NSSA and the application of data fusion in network security are researched comprehensively in this thesis, including the concept description, research state, and developing direction of NSSA, and then the application future of data fusion in network security field, key technologies and its mathematical models are introduced, so the theoretical framework of NSSA using data fusion is formed primarily. Secondly, layered realization model of NSSA is constructed, and then the situational factor extraction model based on evolutionary neural network is constructed, in which evolutionary strategy is used to optimize the neural network parameters. Thirdly, referencing the idea of attributes' importance measure in rough set theory, the mathematical model of NSSA is set up, the quantitative computation of network security situation is achieved, and experiment is done to check the validity of the method. Finally, the relative research in this field is summarized, and the next research points are put forward.