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KRYSTAL: Knowledge graph-based framework for tactical attack discovery in audit data

  • Published:2022-10 Issue: Volume:121 Page:102828
  • ISSN:0167-4048
  • Container-title:Computers & Security
  • language:en
  • Short-container-title:Computers & Security

Author:

Kurniawan KabulORCID,Ekelhart Andreas,Kiesling Elmar,Quirchmayr Gerald,Tjoa A Min

Funder

Christian Doppler Forschungsgesellschaft

Austrian Science Fund

Bundesministerium für Digitalisierung und Wirtschaftsstandort

Österreichische Forschungsförderungsgesellschaft

Österreichische Nationalstiftung für Forschung, Technologie und Entwicklung

Bundesministerium für Verkehr, Innovation und Technologie

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference59 articles.

1. A survey and experimental comparison of distributed SPARQL engines for very large RDF data;Abdelaziz;Proc. VLDB Endowment,2017

2. Counting beyond a Yottabyte, or how SPARQL 1.1 property paths will prevent adoption of the standard;Arenas,2012

3. Capec, 2021Common attack pattern enumeration and classification. Accessed: 30.03.2021, https://capec.mitre.org/.

4. Consortium W.W.W., et al. SPARQL 1.1 overview. 2013. https://www.w3.org/TR/sparql11-overview/.

5. CVE, 2021Common vulnerabilities and exposures. Accessed: 30.03.2021, https://cve.mitre.org/about/index.html.

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2. The Advancement of Knowledge Graphs in Cybersecurity: A Comprehensive Overview;Computational and Experimental Simulations in Engineering;2023-12-01

3. Attack scenario reconstruction via fusing heterogeneous threat intelligence;Computers & Security;2023-10

4. Knowledge graph-based manufacturing process planning: A state-of-the-art review;Journal of Manufacturing Systems;2023-10

5. CAKG: A Framework for Cybersecurity Threat Detection of Automotive via Knowledge Graph;2023 8th International Conference on Data Science in Cyberspace (DSC);2023-08-18
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