A Comparison of Vulnerability Feature Extraction Methods from
Textual Attack Patterns

D 2024

A Comparison of Vulnerability Feature Extraction Methods from Textual Attack Patterns

OTHMAN, Refat T A; Bruno ROSSI a Barbara RUSSO

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

Not specified, 50th Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA), od s. 419-422, 4 s. 2024

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/24:00136709

Organizační jednotka

Fakulta informatiky

ISBN

979-8-3503-8026-2

ISSN

DOI

https://doi.org/10.1109/SEAA64295.2024.00070

UT WoS

001413352200060

EID Scopus

2-s2.0-85200489086

Klíčová slova anglicky

Attack Pattern; Cybersecurity; MITRE; Transformer models; Vulnerability

Štítky

core_B, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 21. 3. 2025 14:58, doc. Bruno Rossi, PhD

Anotace

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.

Citovat

OTHMAN, Refat T A; Bruno ROSSI a Barbara RUSSO. A Comparison of Vulnerability Feature Extraction Methods from Textual Attack Patterns. Online. In 50th Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA). Not specified: IEEE, 2024, s. 419-422. ISBN 979-8-3503-8026-2. Dostupné z: https://doi.org/10.1109/SEAA64295.2024.00070.

@inproceedings{2421880,
   author = {Othman, Refat T A and Rossi, Bruno and Russo, Barbara},
   address = {Not specified},
   booktitle = {50th Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA)},
   doi = {https://doi.org/10.1109/SEAA64295.2024.00070},
   keywords = {Attack Pattern; Cybersecurity; MITRE; Transformer models; Vulnerability},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Not specified},
   isbn = {979-8-3503-8026-2},
   pages = {419-422},
   publisher = {IEEE},
   title = {A Comparison of Vulnerability Feature Extraction Methods from Textual Attack Patterns},
   year = {2024}
}

TY  - CONF
ID  - 2421880
AU  - Othman, Refat T A - Rossi, Bruno - Russo, Barbara
PY  - 2024
TI  - A Comparison of Vulnerability Feature Extraction Methods from Textual Attack Patterns
PB  - IEEE
CY  - Not specified
SN  - 9798350380262
KW  - Attack Pattern
KW  - Cybersecurity
KW  - MITRE
KW  - Transformer models
KW  - Vulnerability
N2  - 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.
ER  -

OTHMAN, Refat T A; Bruno ROSSI a Barbara RUSSO. A Comparison of Vulnerability Feature Extraction Methods from Textual Attack Patterns. Online. In \textit{50th Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA)}. Not specified: IEEE, 2024, s.~419-422. ISBN~979-8-3503-8026-2. Dostupné z: https://doi.org/10.1109/SEAA64295.2024.00070.

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