Scientific Publications

This paper presents a novel framework for real-time cybersecurity threat prediction using advanced machine learning algorithms specifically designed for interconnected Security Operation Centers. The proposed approach demonstrates significant improvements in threat detection accuracy and response time compared to traditional methods.

We propose a novel privacy-preserving framework for cross-border threat intelligence sharing among European Security Operation Centers, addressing the critical challenge of maintaining data privacy while enabling effective collaborative cybersecurity defense.

This study presents an automated incident response system specifically designed for healthcare networks, leveraging machine learning algorithms to rapidly identify and contain cybersecurity threats while maintaining critical patient care services.

A comprehensive analysis of machine learning-enhanced fraud detection systems implemented within banking SOC environments, demonstrating significant improvements in detection rates and reduction in false positives for financial cyber threats.

This workshop paper explores the application of predictive analytics for enhancing cybersecurity in energy grid SCADA systems, presenting early findings from the INTERSOC energy use case implementation.