Abstract
Knowledge representation models have been extensively studied and adopted in many areas such as search, recommendation, etc. However, due to the highly spatio-temporal relevant characteristics of cyberspace security and the dynamic variability of the domain knowledge, the existing models and knowledge embedding methods cannot be adopted in this field directly. In this paper, we propose a two-stream knowledge embedding (TSKE) method for cyberspace security to jointly embed multi-dimensional characteristics. Specifically, we design a static stream neural network and a spatio-temporal stream neural network to extract the static knowledge and the spatio-temporal features of cyberspace security facts, which converts this domain knowledge into vector space. Considering the attack link prediction task in the field of cyberspace security, we conduct extensive experiments and TSKE outperforms other static and dynamic embedding methods.
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Acknowledgment
This work is supported in part by the Major Key Project of PCL (Grant No. PCL2022A03), and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies (2022B1212010005).
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Zhao, A., Wang, H., Zhang, J., Liu, Y., Ma, C., Gu, Z. (2024). TSKE: Two-Stream Knowledge Embedding for Cyberspace Security. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14332. Springer, Singapore. https://doi.org/10.1007/978-981-97-2390-4_10
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DOI: https://doi.org/10.1007/978-981-97-2390-4_10
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