Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Ryan Barron, Maksim E. Eren, Manish Bhattarai, Selma Wanna, Nicholas Solovyev, Kim Rasmussen, Boian S. Alexandrov, Charles Nicholas, Cynthia Matuszek
March 2024
Preprint PDF Details

Abstract

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.

Type
Conference paper
Publication
In IEEE 12th International Symposium on Digital Forensics and Security (ISDFS), 2024

Keywords:

non-negative matrix factorization, cyber-security, knowledge graph, topic modeling

Citation:

R. Barron, M. E. Eren, M. Bhattarai, S. Wanna, N. Solovyev, K. Rasmussen, B. S. Alexandrov, C. Nicholas, and C. Matuszek. “Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization,” 2024 IEEE International Symposium on Digital Forensics and Security (ISDFS), 2024, pp. 1-6.

BibTeX:

@INPROCEEDINGS{barronISDFS2024,
  author={R. {Barron} and M. E. {Eren} and M. {Bhattarai} S. {Wanna} and N. {Solovyev} and K. {Rasmussen} and B. S. {Alexandrov} and C. {Nicholas} and C. {Matuszek}},
  booktitle={IEEE International Symposium on Digital Forensics and Security (ISDFS)}, 
  title={Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization}, 
  year={2024},
  volume={},
  number={},
  pages={1-6},
  doi={}}
non-negative matrix factorization cyber-security knowledge graph topic modeling
Maksim E. Eren
Maksim E. Eren
Scientist

My research interests lie at the intersection of the machine learning and cybersecurity disciplines, with a concentration in tensor decomposition.