Research on hierarchical network security situation awareness data fusion method in big data environment

ABSTRACT

With the increase in the number of network users, data information has become more abundant, and query speed and coverage have greatly improved. In this era, the problem of information leakage is relatively serious, and some malicious software has invaded the network system of user devices, increasing the threat to network security. In order to improve the analysis ability of network security situation awareness, this study designs a hierarchical network security situation awareness data fusion method under the big data environment. On the basis of data fusion technology and network security situation awareness technology, the hierarchical network security situation information is collected. Then, its features are extracted, and the network security situation awareness process is constructed based on RBF neural. By using this process, hierarchical network security situation awareness data can be obtained. The data is first sorted out and analyzed, and then the data is filtered. Finally, the matrix three decomposition data fusion algorithm is selected as the blueprint of the data fusion method. It can complete the hierarchical network security situation awareness data fusion. Experimental results show that the proposed method has high matching accuracy, little influence on the data fusion time due to the increase of data volume and low energy consumption in the fusion process.

KEYWORDS:

• Big data technology
• hierarchical network
• security situational awareness
• data characteristics
• pretreatment

Acknowledgments

The research is supported by: Department of Science and Technology of Guangdong Province Guangdong Digital Chemical Plant Engineering Technology Research Center 2018-KJZX014; Department of Education of Guangdong Province Research and application of vocational education student practice management system based on blockchain 2021KTSCX338.

Disclosure statement

No potential conflict of interest was reported by the author(s).