Aug. 26, 2022, 1:10 a.m. | Vittorio Orbinato, Mariarosaria Barbaraci, Roberto Natella, Domenico Cotroneo

cs.LG updates on arXiv.org arxiv.org

Proactive approaches to security, such as adversary emulation, leverage
information about threat actors and their techniques (Cyber Threat
Intelligence, CTI). However, most CTI still comes in unstructured forms (i.e.,
natural language), such as incident reports and leaked documents. To support
proactive security efforts, we present an experimental study on the automatic
classification of unstructured CTI into attack techniques using machine
learning (ML). We contribute with two new datasets for CTI analysis, and we
evaluate several ML models, including both traditional …

arxiv cyber cyber threat experimental intelligence mapping study threat intelligence

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne