# Malware Knowledge Graph Generation

arXiv: https://arxiv.org/abs/2102.05583 · HTML: https://mlsanity.com/paper/2102.05583/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2021.

## Built on by (most cited first, 1 of 1)

- [Web Scale Graph Mining for Cyber Threat Intelligence](https://mlsanity.com/paper/2411.06239/) (2024)

## Abstract (arXiv)

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 textsfTINKER - a knowledge graph for threat intelligence (Threat INtelligence KnowlEdge gRaph). textsfTINKER 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 textsfTINKER 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 textsfTINKER.