Understanding Cybersecurity Threat Trends Through Dynamic Topic Modeling Article Swipe
Jennifer Sleeman
,
Tim Finin
,
Milton Halem
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2021.601529
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2021.601529
Cybersecurity threats continue to increase and are impacting almost all aspects of modern life. Being aware of how vulnerabilities and their exploits are changing gives helpful insights into combating new threats. Applying dynamic topic modeling to a time-stamped cybersecurity document collection shows how the significance and details of concepts found in them are evolving. We correlate two different temporal corpora, one with reports about specific exploits and the other with research-oriented papers on cybersecurity vulnerabilities and threats. We represent the documents, concepts, and dynamic topic modeling data in a semantic knowledge graph to support integration, inference, and discovery. A critical insight into discovering knowledge through topic modeling is seeding the knowledge graph with domain concepts to guide the modeling process. We use Wikipedia concepts to provide a basis for performing concept phrase extraction and show how using those phrases improves the quality of the topic models. Researchers can query the resulting knowledge graph to reveal important relations and trends. This work is novel because it uses topics as a bridge to relate documents across corpora over time.
Related Topics
Concepts
Computer science
Exploit
Inference
Data science
Knowledge graph
Process (computing)
Domain knowledge
Graph
Domain (mathematical analysis)
Bridge (graph theory)
Phrase
Information retrieval
Computer security
Artificial intelligence
Theoretical computer science
Operating system
Internal medicine
Mathematical analysis
Medicine
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fdata.2021.601529
- https://www.frontiersin.org/articles/10.3389/fdata.2021.601529/pdf
- OA Status
- gold
- Cited By
- 15
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3174186907
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3174186907Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fdata.2021.601529Digital Object Identifier
- Title
-
Understanding Cybersecurity Threat Trends Through Dynamic Topic ModelingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-29Full publication date if available
- Authors
-
Jennifer Sleeman, Tim Finin, Milton HalemList of authors in order
- Landing page
-
https://doi.org/10.3389/fdata.2021.601529Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fdata.2021.601529/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fdata.2021.601529/pdfDirect OA link when available
- Concepts
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Computer science, Exploit, Inference, Data science, Knowledge graph, Process (computing), Domain knowledge, Graph, Domain (mathematical analysis), Bridge (graph theory), Phrase, Information retrieval, Computer security, Artificial intelligence, Theoretical computer science, Operating system, Internal medicine, Mathematical analysis, Medicine, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 3, 2023: 1, 2022: 4Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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