Inferring Discussion Topics about Exploitation of Vulnerabilities from Underground Hacking Forums

TL;DR

This paper presents a machine learning approach using topic modeling to analyze underground hacking forums, aiming to automatically detect and classify discussions on vulnerabilities and exploits to identify emerging threats.

Contribution

The study introduces a novel application of Latent Dirichlet Allocation to uncover key themes in underground forums for proactive cybersecurity threat detection.

Findings

Identified prevalent vulnerability discussions

Uncovered emerging exploit techniques

Enabled automatic classification of forum content

Abstract

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.