Our Projects
Framework to Detect, Monitor, and Analyze the Evolution of Multistage Attacks During Runtime
This project aims to develop a comprehensive framework designed to detect, monitor, and analyze the progression of multistage cyber attacks in real-time. The framework will leverage advanced techniques in cybersecurity, including machine learning and artificial intelligence, to identify and respond to complex attack patterns as they unfold.
Project Code: ANID Fondecyt project 11221155 [ongoing - closing by March 2025]
Intrusion.aware: Comprehensive Platform to Detect and Respond to Cyberattacks Using Responsible Artificial Intelligence
The Intrusion.aware project aims to develop an integrated platform designed to detect and respond to cyberattacks using responsible artificial intelligence (AI). This platform will leverage advanced AI techniques to identify and mitigate cyber threats in real-time, ensuring robust protection for IT infrastructures. The project focuses on incorporating ethical AI practices to ensure that the solutions developed are not only effective but also align with principles of fairness, transparency, and accountability. By providing tools for real-time monitoring and response, the Intrusion.aware platform seeks to enhance the overall security posture of organizations, making them more resilient to evolving cyber threats. The previous version of this project was TRLUp Intrusion Aware++, which was funded internally by the Innovation Unit of the Faculty of Engineering and Sciences (FIC). This internal funding supported the development of a level 4 prototype of the product, which enabled the team to secure the current FONDEF project.
Project Code: ANID Fondef IT24I0144 [ongoing - closing by October 2026]