Software Engineer and Cybersecurity Researcher from Spain 🇪🇸 specializing in Cyber Threat Intelligence and Adversary Emulation, using offensive methodologies to simulate real-world attacks and improve defensive capabilities against evolving threats.

• 🌟 Collapse - A small, fast file compressor
• 🔑 Tanukeys - Service for storing cryptographic public keys
• 🦊 Fennec - Another Cyber Threat Intelligence platform
• 🦀 Rust DDD Skeleton - Rust template structured with DDD and CQRS principles
• 🐍 Python DDD Skeleton - FastAPI template structured with DDD and CQRS principles
• 🥟 Bun DDD Skeleton - Bun template structured with DDD and CQRS principles

• 🍃 MongoExport - Incrementally export MongoDB collections
• 🔍 Deepfinder - Search attributes in nested dict/list structures using dot-path syntax
• 🪞 Kagami - GitHub Action to mirror a repository to another git system

• 🐋 Maruja - Docker container packet sniffer
• 🐠 Datarium - Common data structures for TypeScript
• 💊 Python Value Objects - Lightweight value objects for domain modeling in Python
• 🆚 DeepComparer - Deep comparison of nested Python data structures
• ⚙️ Storable Functions - Store and execute JavaScript functions as reusable values
• 🔍 DeepfinderJS - Search attributes in nested dict/list structures using dot-path syntax
• 🦝 TanukiKeyGuardian - KMS web service for secure key exchange and cryptographic isolation
• 🇯🇵 Kanjinary - Kanji learning web app
• 🍥 Mikana - Android app to learn Japanese syllabaries
• 🀄 Kanami - Practice Japanese syllabaries, Hiragana and Katakana

• Adaptive Adversary Emulation Framework for Cyberwarfare Operations (COMMAG2026) - A modular framework, OPFOR (Operational Platform for Offensive Replication), for executing Adversary Emulation based on behaviors attributable to real threat actors, integrated within the Cyber Threat Hunting paradigm to protect Critical Infrastructures. It formalizes the concept of Adaptive Adversary Emulation (AAE), combining CTI with an internal understanding of network topology weaknesses to generate contextualized, infrastructure-aware attack paths. A Stuxnet-inspired operation, executed by a Reinforcement Learning agent on a simulated nuclear plant network, validates the approach and informs defensive prioritization and early detection strategies.

• Adaptive Adversary Emulation Applied to Critical Infrastructures and IIoT Networks (JNIC2026) - A solution based on Adversary Emulation (AE) that dynamically adapts attack tactics to the targeted infrastructure, balancing faithful imitation of the emulated adversary with adaptability to the target environment. Guided by Machine Learning and industrial simulation, it enables an AI agent to train efficiently by learning from the environment and its weaknesses, supporting threat hunting and proactive defense evaluation in critical infrastructures and IIoT settings.

• Digital Twin for Adaptive Adversary Emulation in IIoT Control Networks (ESORICS2025) - A framework that uses Digital Twins to emulate adaptive cyber adversaries in IIoT Control Networks. It creates a virtual replica of the industrial system where automated attackers, driven by Reinforcement Learning, interact with the environment and adapt their strategies to exploit critical nodes, generating realistic and dynamic attack scenarios without impacting real infrastructure. It enables the evaluation of defensive mechanisms and supports proactive cybersecurity testing in critical industrial networks.