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Communication Dans Un Congrès Année : 2024

Matching Knowledge Graphs for Cybersecurity Countermeasures Selection

Christopher Neal
  • Fonction : Auteur
  • PersonId : 1404264
Frédéric Cuppens
  • Fonction : Auteur
  • PersonId : 1099936
Nora Cuppens-Boulahia
  • Fonction : Auteur
  • PersonId : 1100401
Makhlouf Hadji

Résumé

As cyberattacks continue to increase, detecting and performing remediation actions against them is essential. This paper presents an approach to automate the countermeasures selection process to deal with a vulnerability exploitation performed by a cyberattack. We propose an approach to match two knowledge graphs, one from a vulnerability ontology, Vulnerability Description Ontology (VDO), and the other is the countermeasures knowledge graph, D3FEND, to mitigate cyberattack impacts. Our approach uses machine learning and an inference system to match entities from VDO and D3FEND to select candidate countermeasures to an attack. Our contribution aims to automatically select countermeasures intended to be part of an incident response playbook for a vulnerability. We show our approach application to a WannaCry use-case scenario.We validate our countermeasures selection approach by comparing the countermeasures automatically selected with those proposed in the literature for a WannaCry attack.
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Dates et versions

hal-04671226 , version 1 (28-08-2024)

Identifiants

  • HAL Id : hal-04671226 , version 1

Citer

Kéren A Saint-Hilaire, Christopher Neal, Frédéric Cuppens, Nora Cuppens-Boulahia, Makhlouf Hadji. Matching Knowledge Graphs for Cybersecurity Countermeasures Selection. 6th International Conference Science of Cyber Security (SciSec), Aug 2024, Copenhagen, Denmark. ⟨hal-04671226⟩

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