A Comparison of Vulnerability Feature Extraction Methods from Textual Attack Patterns

OTHMAN, Refat T A, Bruno ROSSI and Barbara RUSSO. A Comparison of Vulnerability Feature Extraction Methods from Textual Attack Patterns. In 50th Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA). IEEE, 2024.

Basic information

Original name

A Comparison of Vulnerability Feature Extraction Methods from Textual Attack Patterns

Authors

OTHMAN, Refat T A, Bruno ROSSI and Barbara RUSSO

Edition

50th Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA), 2024

Publisher

IEEE

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Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Confidentiality degree

není předmětem státního či obchodního tajemství

Tags

International impact, Reviewed

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Abstract

V originále

Nowadays, threat reports reported by cybersecurity vendors incorporate detailed descriptions of attacks within unstructured text. Knowing vulnerabilities that are related to these reports helps cybersecurity researchers and practitioners understand and adjust to evolving attacks and develop mitigation plans for them. This paper aims to aid cybersecurity researchers and practitioners in choosing attack extraction methods to enhance the monitoring and sharing of threat intelligence. In this work, we examine five existing extraction methods and find that Term Frequency-Inverse Document Frequency (TFIDF) outperforms the other four methods with a precision of 75% and an F1 score of 64%. We obtain that when we increase the class labels, all methods perform worse regarding F1 score drops. The findings offer valuable insights to the cybersecurity community, and our research can aid cybersecurity researchers in evaluating and comparing the effectiveness of upcoming extraction methods.