The increasing sophistication of cyber threats necessitates proactive
measures to identify vulnerabilities and potential exploits. Underground
hacking forums serve as breeding grounds for the exchange of hacking techniques
and discussions related to exploitation. In this research, we propose an
innovative approach using topic modeling to analyze and uncover key themes in
vulnerabilities discussed within these forums. The objective of our study is to
develop a machine learning-based model that can automatically detect and
classify vulnerability-related discussions in underground hacking forums. By
monitoring and analyzing the content of these forums, we aim to identify
emerging vulnerabilities, exploit techniques, and potential threat actors. To
achieve this, we collect a large-scale dataset consisting of posts and threads
from multiple underground forums. We preprocess and clean the data to ensure
accuracy and reliability. Leveraging topic modeling techniques, specifically
Latent Dirichlet Allocation (LDA), we uncover latent topics and their
associated keywords within the dataset. This enables us to identify recurring
themes and prevalent discussions related to vulnerabilities, exploits, and
potential targets.

通过主题建模分析并发现地下黑客论坛中讨论的漏洞的关键主题，从而开发出一种基于机器学习的模型，能够自动检测和分类地下黑客论坛中与漏洞相关的讨论。