Comparative Evaluation of NLP-Based Approaches for Linking CAPEC Attack Patterns from CVE Vulnerability Information

Kenta Kanakogi*, Hironori Washizaki, Yoshiaki Fukazawa, Shinpei Ogata, Takao Okubo, Takehisa Kato, Hideyuki Kanuka, Atsuo Hazeyama, Nobukazu Yoshioka

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Vulnerability and attack information must be collected to assess the severity of vulnerabilities and prioritize countermeasures against cyberattacks quickly and accurately. Common Vulnerabilities and Exposures is a dictionary that lists vulnerabilities and incidents, while Common Attack Pattern Enumeration and Classification is a dictionary of attack patterns. Direct identification of common attack pattern enumeration and classification from common vulnerabilities and exposures is difficult, as they are not always directly linked. Here, an approach to directly find common links between these dictionaries is proposed. Then, several patterns, which are combinations of similarity measures and popular algorithms such as term frequency–inverse document frequency, universal sentence encoder, and sentence BERT, are evaluated experimentally using the proposed approach. Specifically, two metrics, recall and mean reciprocal rank, are used to assess the traceability of the common attack pattern enumeration and classification identifiers associated with 61 identifiers for common vulnerabilities and exposures. The experiment confirms that the term frequency–inverse document frequency algorithm provides the best overall performance.

Original languageEnglish
Article number3400
JournalApplied Sciences (Switzerland)
Issue number7
Publication statusPublished - 2022 Apr 1


  • CVE
  • TF-IDF
  • cybersecurity database
  • natural language processing
  • sentence BERT
  • sentence embeddings
  • universal sentence encoder

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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