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Applicability Analysis of Knowledge Graph Embedding on Blended Threat
  • Lee, Minkyung ;
  • Jung, In Su ;
  • Kim, Deuk Hun ;
  • Jang-Jaccard, Julian ;
  • Kwak, Jin
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Publication Year
2022-01-01
Journal
2022 International Conference on Platform Technology and Service, PlatCon 2022 - Proceedings
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2022 International Conference on Platform Technology and Service, PlatCon 2022 - Proceedings, pp.48-52
Keyword
blended threatiobeknowledge embedding
Mesh Keyword
Applicability analysisBlended threatsGraph embeddingsHighly denseIobeKnowledge embeddingKnowledge graphsNetwork technologiesSensing technologySmart grid
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Science ApplicationsHardware and ArchitectureInformation SystemsInformation Systems and ManagementControl and Optimization
Abstract
With the advent of massive IoT in which objects and humans form highly dense interconnections, the development of new technologies and platforms has been significantly accelerated. In addition, networks and sensing technologies are being blended in various contexts, such as smart factories, digital health, and smart grids, etc. This hyper-connectivity of the blended environment has caused the diversification of the IoT environment and architecture and led to attack surface. Accordingly, the complexity of analyzing and responding to the security breaches is increasing. Hence, recent research has been focused on responding to the potential attack routes using a knowledge graph, a concept that is used to analyze the correlations between the threat data and potentially attackable asset data. However, as the analysis utilizes a single dataset, it has limitation in analyzing and predicting complex threat information. Therefore, to predict and respond to the potential complex security risks on IoBE, a knowledge graph embedding model applicable to blended threats is analyzed in this study.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36830
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142236761&origin=inward
DOI
https://doi.org/10.1109/platcon55845.2022.9932052
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9932030
Type
Conference
Funding
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1A2C2011391).
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