Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling

Sikos, Leslie F.

doi:10.1007/978-3-030-67681-0_4

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…Recent studies have demonstrated the application of knowledge graphs in identifying emerging trends, uncovering hidden connections, and predicting future research directions within scientific domains [4]- [11]. The integration of semantic analysis with graph-based structures allows for a more nuanced understanding of the thematic evolution in fields such as deep learning and cybersecurity [12], [13]. These advances highlight the potential of knowledge graphs to transform the exploration and exploitation of scientific literature, moving beyond traditional search mechanisms to enable a deeper, contextually rich engagement with content [14], [15].…”

Section: A Knowledge Graphsmentioning

confidence: 99%

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Barron,

Eren,

Bhattarai

et al. 2024

2024 12th International Symposium on Digital Forensics and Security (ISDFS)

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Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG.

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Section: A Knowledge Graphsmentioning

confidence: 99%

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Barron,

Eren,

Bhattarai

et al. 2024

2024 12th International Symposium on Digital Forensics and Security (ISDFS)

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“…Jia et al [21] proposed a framework to generate a cybersecurity knowledge base by utilizing an ontology based on vulnerabilities and by using the Stanford Named Entity Recognizer (NER) 50 and conditional random fields (CRFs) to extract cybersecurity entities from unstructured data. These are expressed in RDF, similar to the structured data (which is directly written in RDF).…”

Section: Knowledge Graph-based Koses For Cybersecurity Applicationsmentioning

confidence: 99%

“…-the nodes are cybersecurity concepts and properties, and the arcs are correlations between them [50]; -the nodes are network device types and their properties, the arcs are connections between them; -the nodes are vulnerabilities and the arcs define properties, such as vulnerability scoring, weaknesses, and platforms [26].…”

Section: Introduction To Cybersecurity Knowledge Graphsmentioning

confidence: 99%

Cybersecurity knowledge graphs

Sikos

2023

Knowl Inf Syst

Self Cite

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Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based data model, provide holistic approaches for processing massive volumes of complex cybersecurity data derived from diverse sources. They can assist security analysts to obtain cyberthreat intelligence, achieve a high level of cyber-situational awareness, discover new cyber-knowledge, visualize networks, data flow, and attack paths, and understand data correlations by aggregating and fusing data. This paper reviews the most prominent graph-based data models used in this domain, along with knowledge organization systems that define concepts and properties utilized in formal cyber-knowledge representation for both background knowledge and specific expert knowledge about an actual system or attack. It is also discussed how cybersecurity knowledge graphs enable machine learning and facilitate automated reasoning over cyber-knowledge.

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Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling

Cited by 2 publications

References 32 publications

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Cybersecurity knowledge graphs

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