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arXiv · 2102.05583

Malware Knowledge Graph Generation

Sharmishtha Dutta·Nidhi Rastogi·Destin Yee·Chuqiao Gu·Qicheng Ma
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Abstract

Cyber threat and attack intelligence information are available in non-standard format from heterogeneous sources. Comprehending them and utilizing them for threat intelligence extraction requires engaging security experts. Knowledge graphs enable converting this unstructured information from heterogeneous sources into a structured representation of data and factual knowledge for several downstream tasks such as predicting missing information and future threat trends. Existing large-scale knowledge graphs mainly focus on general classes of entities and relationships between them. Open-source knowledge graphs for the security domain do not exist. To fill this gap, we've built \textsf{TINKER} - a knowledge graph for threat intelligence (\textbf{T}hreat \textbf{IN}telligence \textbf{K}nowl\textbf{E}dge g\textbf{R}aph). \textsf{TINKER} is generated using RDF triples describing entities and relations from tokenized unstructured natural language text from 83 threat reports published between 2006-2021. We built \textsf{TINKER} using classes and properties defined by open-source malware ontology and using hand-annotated RDF triples. We also discuss ongoing research and challenges faced while creating \textsf{TINKER}.

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Sharmishtha Dutta, Nidhi Rastogi, Destin Yee, Chuqiao Gu, Qicheng Ma. 2021-02-10. Malware Knowledge Graph Generation. https://doi.org/10.13140/rg.2.2.27340.95367

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