A Comparison of Vulnerability Feature Extraction Methods from Textual Attack Patterns

OTHMAN, Refat T A, Bruno ROSSI a 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.

Základní údaje

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

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Stať ve sborníku
• Obor: 10200 1.2 Computer and information sciences
• Utajení: není předmětem státního či obchodního tajemství
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.