Abstract
Knowledge base of dynamic risk control strategy based on immunity is a significant effect on effective analysis and defense against illegal network intrusion. How to realize the automatic understanding and processing of computers with control strategy knowledge is of great significance for quickly responding to network security risks. As a kind of knowledge representation tool, ontology can provide support for knowledge sharing, reuse and automatic computer understanding in specific fields, and has been widely used in various fields. This paper first introduces the immune-based network dynamic risk control model and network dynamic risk quantitative evaluation. And then, according to the ontology modeling method of network dynamic risk control strategy knowledge, this paper extracts domain knowledge concepts, attributes, relationships, instances, etc., and constructs domain ontology model, application ontology model, and atom ontology model for the network dynamic risk control strategy knowledge. These ontology models are represented using semantic Web ontology expression languages PDF and OWL, and are constructed using the protégé ontology editing tool. Finally, the important concepts in the knowledge of network dynamic risk control strategy and the relationship between concepts are expressed in the form of graph, so as to help the network security analysts and decision makers to effectively control and make decisions.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of China (Grant No. U1736212, No. 61572334, No. 61872255), in part by the Sichuan Province Key Research & Development Project of China (Grant No. 2018GZ0183), in part by the Fundamental Research Funds for the Central Universities, and in part by the National key research and development program of China (Grant No. 2016YFB0800600).
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Huang, M., Li, T., Zhao, H., Liu, X., Gao, Z. (2020). Immune-Based Network Dynamic Risk Control Strategy Knowledge Ontology Construction. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_30
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DOI: https://doi.org/10.1007/978-3-030-52243-8_30
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