Research on multi-source heterogeneous data structure analysis technique based on AI security detection algorithm

Chunyan Yang; Songming Han; Jieke Lu; Shaofeng Ming; Wei Zhang

doi:10.1117/12.3032167

8 June 2024 Research on multi-source heterogeneous data structure analysis technique based on AI security detection algorithm

Chunyan Yang, Songming Han, Jieke Lu, Shaofeng Ming, Wei Zhang

Author Affiliations +

Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 131710M (2024) https://doi.org/10.1117/12.3032167
Event: Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 2024, Jinan, China

Abstract

The relationships between multi-source heterogeneous data and elements in the field of artificial intelligence security are integrated and analyzed in this paper, including attack information, data information, and other security data. Targeting the associated complex entity concepts that existed in the construction of the artificial intelligence security knowledge graph, the ontology structure is divided into theory layer, problem layer, and measure layer, making the artificial intelligence security ontology more diverse and expandable. The addition of the measure layer provides more accurate security decision-making reasoning for the subsequent knowledge inference stage.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chunyan Yang, Songming Han, Jieke Lu, Shaofeng Ming, and Wei Zhang "Research on multi-source heterogeneous data structure analysis technique based on AI security detection algorithm", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 131710M (8 June 2024); https://doi.org/10.1117/12.3032167

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