Predicting future threats
Predicting attacks can help to prevent them or at least reduce their impact. Nowadays, existing attack prediction methods make accurate predictions only hours in advance or cannot predict geo-politically motivated attacks.
ThreatPredict aims to predict different attack types days in advance. It develops machine-learning algorithms that capture spatio-temporal dynamics of cyber-attacks and global social, geo-political and technical events. Various sources of information are collected, enriched and correlated such as honeypot data, darknet, GDELT, Twitter, and vulnerability databases. In addition to warning about attacks, this project will improve our understanding of the effect of global events on cyber-security.
An international project
This project involved three countries with three academic partners to deliver high-quality research: Inria in France, International University of Rabat in Morocco, and Carnegie Mellon University in USA.
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Funding
This project is funded by Science for Peace and Security programme of NATO.
