Activities

Adaptive Adversary Emulation Framework for Cyberwarfare Operations was published online in IEEE Communications Magazine (vol. 64, no. 10, October 2026, pp. 54–59). It introduces OPFOR, a modular adversary emulation framework that combines cyber threat intelligence with knowledge of the network topology to generate infrastructure-aware attack paths, validated with a Stuxnet-inspired operation run by a reinforcement-learning agent on a simulated nuclear plant network. It is joint work with Cristina Alcaraz, Mario Reyes and Javier Lopez.

Refreshed the site’s aesthetic and migrated the whole stack from Jekyll to Nuxt 3 with Nuxt Content and Tailwind. Cleaner typography, faster builds and a much nicer editing workflow for future updates.

Adaptive Adversary Emulation Applied to Critical Infrastructures and IIoT Networks was published in the proceedings of the Jornadas Nacionales de Investigación en Ciberseguridad (JNIC 2026), held 6–8 May 2026 at the Universitat Politècnica de Catalunya in Barcelona. It presents an adversary emulation approach that adapts its attack tactics to the targeted infrastructure, guided by machine learning and industrial simulation so that an AI agent learns from the environment and its weaknesses. It is joint work with Mario Reyes and Javier Lopez.

The new personal website is now online, featuring my publications, projects and academic information. A centralized space to share my work in cybersecurity and cyber intelligence.

Digital Twin for Adaptive Adversary Emulation in IIoT Control Networks was published online in Computer Security – ESORICS 2025 (Springer LNCS, pp. 423–442). It uses digital twins as the target of adversary emulation: reinforcement-learning attackers adapt to the industrial system and go after its most critical nodes, producing realistic attack scenarios without touching the real infrastructure. It is joint work with Cristina Alcaraz, Javier Lopez, Juan Caubet and Rodrigo Román.