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Knowledge mining of unstructured information: application to cyber domain.

Tuomas Takko1, Kunal Bhattacharya2,3, Martti Lehto4

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This study introduces a new knowledge graph and mining framework to extract cyber-attack information from text. The system accurately identifies cyber threats and estimates attack incidence, aiding analysts in understanding cyber risks.

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Area of Science:

  • Cybersecurity
  • Data Science
  • Information Extraction

Background:

  • Vast amounts of cyber-incident data exist online.
  • Processing this data manually is challenging for analysts.
  • New methods are needed for efficient cyber threat intelligence.

Purpose of the Study:

  • To present and implement a novel knowledge graph and mining framework.
  • To extract relevant information from free-form text about cyber incidents.
  • To estimate cyberattack incidence using extracted knowledge graphs.

Main Methods:

  • A machine learning pipeline generates graphs of entities (organizations, countries, etc.).
  • A non-technical cyber-ontology is used for knowledge extraction.
  • Publicly available cyber-incident reports are used for testing.

Main Results:

  • Knowledge extraction accuracy was found to be sufficient.
  • The graph-based threat estimation correlated with actual attack records.
  • The framework enables inference of cyber risks and propagation.

Conclusions:

  • The developed framework effectively extracts and analyzes cyber-incident data.
  • It provides valuable insights into the cyber threat landscape.
  • Analysts can use this tool to assess risks to various entities and industries.