  Nowadays, threat reports from 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. 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 feature extraction methods (TF-IDF,
LSI, BERT, MiniLM, RoBERTa) and find that Term Frequency-Inverse Document
Frequency (TF-IDF) outperforms the other four methods with a precision of 75\%
and an F1 score of 64\%. 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.
