Abstract
Knowledge graph plays an important role in semantic search, data analysis and intelligent decision making, and has made remarkable achievements in many fields. However, it is rarely used in the field of network security, which hinders the systematic and structured development of network space security. In order to build a cyberspace security knowledge system to fill the gaps in this field, and to visualize cyberspace security knowledge, this paper proposes a construction method of network space security knowledge map, and uses bottom-up method to construct network security knowledge system. Firstly, Protégé is used to construct ontology of cyberspace security knowledge. Secondly, semantic relations between entities are extracted from cyberspace security data. Finally, the network security knowledge system is stored and displayed by Neo4j graphics database. The experimental results show that the method is effective and has important significance for the development of network space security.
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References
Xu, Z.L., Sheng, Y.P., He, L.R., Wang, Y.F.: Review on knowledge graph techniques. J. Univ. Electron. Sci. Technol. Chin. 45, 589–606 (2016)
Liu, Q., Li, Y., Hong, D., Yao, L.: Knowledge graph construction techniques. J. Comput. Res. Develop. 53, 582–600 (2016)
Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum. Comput. Stud. 43, 907–928 (1995)
Syed, Z., Padia, T., Finin, L., Mathews, A., Joshi: UCO: a unified cybersecurity ontology. In: Workshop on Artificial Intelligence for Cyber Security (2016)
Zhang, K., Liu, J.J.: Ontology construction for security analysis of network nodes. In: 2020 International Conference on Communications, Information System and Computer Engineering (CISCE), pp. 292–297 (2020)
Xiong, F., Gao, J.L.: Entity alignment for cross-lingual knowledge graph with graph convolutional networks. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 6480–6481 (2019)
Wang, X., Wang, D., Canran, X., He, X., Cao, Y., Chua, T.-S.: Explainable reasoning over knowledge graphs for recommendation. Proc. AAAI Conf. Artif. Intel. 33, 5329–5336 (2019)
Bollacker, K., Evans, C., Paritosh, P.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)
Lehmann, J., Isele, R., Jakob, M., et al.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)
Manikandan, R., Madgula, K., Saha, S., et al.: Dbpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Seman. Web 6, 167–195 (2015)
Sikos, L.F., Stumptner, M., Mayer, W., Howard, C., Voigt, S.: Automated reasoning over provenance-aware communication network knowledge in support of cyber-situational awareness. In: International Conference on Knowledge Science, Engineering and Management, pp. 132–143 (2018)
Li, T., Li, H., Zhong, S., et al.: Automated reasoning over provenance-aware communication network knowledge in support of cyber-situational awareness. In: International Conference on Knowledge Science, Engineering and Management, pp. 132–143 (2018)
Yoo, S., Jeong, O.: Bot: empathetic chatbot using auto-growing knowledge graph. Comput. Mater. Continua 67(3), 2807–2817 (2021)
Pingle, A., Piplai, A., Mittal, S., Joshi, A., Holt, J., Zak, R.: RelExt: relation extraction using deep learning approaches for cybersecurity knowledge graph improvement. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 879–886 (2019)
Ahn, Y., Jeong, O.: Time-Aware PolarisX: auto-growing knowledge graph. Comput. Mater. Continua 67(3), 2695–2708 (2021)
Yue, Q., Li, X., Li, D.: Chinese relation extraction on forestry knowledge graph construction. Comput. Syst. Sci. Eng. 37(3), 423–442 (2021)
Noy, N.F., Mcguinness, D.L.: Ontology development 101: a guide to creating your first ontology (2001)
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–411 (2004)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Acknowledgement
This work was supported by the National Natural Science Foundation of China: (No: 61872203, No: 61802212), Shandong Provincial Natural Science Foundation: (No: ZR2019BF017, No: Z R2020MF054), the Jinan City ‘20 universities’ Funding Projects (No: 2019GXRC031, No: 2020GXR C056), Provincial Educational Reform Project (NO: Z2020042), School-level teaching reform project (NO: 201804), and School-level key project (NO: 2020zd24).
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Ma, B., Li, D., Wang, C., Li, J., Li, G., Cui, X. (2022). A Cyberspace Security Knowledge System Based on Knowledge Graph. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_28
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DOI: https://doi.org/10.1007/978-3-031-06791-4_28
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