Abstract
Interpreting cyber-knowledge is challenging due to the data heterogeneity issues typical to data aggregation from disparate data sources. This chapter demonstrates knowledge graph-based techniques to capture and reason over provenance for communication networks at different levels of granularity, thereby getting the best of both worlds, structured data representation and provenance-awareness, at the same time.
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Notes
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The term was popularized by Google when introducing their proprietary knowledge base, Google Knowledge Graph, in 2012 to enhance the value of information returned by Google Web Search queries based on aggregating data from unstructured, semistructured, and structured data sources (Sikos 2015).
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This is common due to (1) the different naming conventions used by the various data sources, such as routing messages and router configuration files, and (2) some graph nodes are initially blank because of the unavailability of the proper/descriptive name.
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This is because there are many vulnerabilities related to each attack type.
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Sikos, L.F. (2021). Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling. In: Sikos, L.F., Seneviratne, O.W., McGuinness, D.L. (eds) Provenance in Data Science. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-030-67681-0_4
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