Semantically Modeling Cyber Influence Campaigns (CICs): Ontology Model and Case Studies

Nathan Johnson, Benjamin Turnbull, Thomas Maher, Martin Reisslein

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a novel ontological model of Cyber Influence Campaigns (CICs). The model accepts both physical and cyber based actions. The model represents the mechanics, linkages, and structure of tailored data to concurrently analyse both the physical and cyber realms. Influence modeling and ontological based analysis of social media has to date mainly focused on the use of ontologies to categorise or cluster the results of text based feature extraction. Whilst this is highly important for detection of misinformation that has the potential to influence a network, these methods do not provide a mechanism for mapping events across realms in order to quantify the influence in meaningful ways. By developing a novel semantic model and unique classes that leverage the graph nature of the ontological representation, our ontological model provides causal linkages and a framework which is applicable for analysis and deeper insights into CICs. This study also builds two tailored datasets for our ontological model from raw Twitter data as the IEEE DataPort Cyber Influence Campaign Ontology dataset (DOI 10.21227/70kc-yx38) and details how to analyze various CIC scenarios.

Original languageEnglish (US)
Article number9311246
Pages (from-to)9365-9382
Number of pages18
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

  • Cyber influence
  • conflict
  • influence campaign
  • ontology
  • semantic model
  • social media

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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