Speaker
Description
Cybersecurity threats are increasingly orches-
trated on hidden and encrypted digital platforms (e.g., Tele-
gram channels and dark web forums). This trend creates
significant challenges for organizations that need timely
threat intelligence from such closed communities. In this
paper, we propose a framework for monitoring and analyz-
ing cybersecurity-related conversations across public and
private online spaces. Our approach integrates advanced
data collection techniques with natural language processing
(NLP) and smart correlation to extract actionable threat
intelligence in real time.The actual work presented in
this paper assumes a GPU-optimised multilingual NLP
pipeline and and the current results resume to forming
a comprehensive english dataset that will go through a
Filtering and Correlation pipeline, that will be used to
extract and generate threat intelligence reports and alerts
about trends in threat actor discussions.
