An autonomous and Distributed Deep Learning Intrusion Detection System for Internet of Things (IoT)
HYDRANOS aims to revolutionize computing system security with an integrated, full-stack approach that addresses diverse cross-layer attacks. By integrating reconfiguration capabilities into the underlying System-on-Chip (SoC), it enables post-silicon hardware updating and patching, establishing a holistic framework for enhanced hardware-assisted security and more robust computing systems.
CROSSING provides cryptography-based security solutions for new and next-generation computing environments, ensuring efficiency, robustness, and ease of use for developers, administrators, and end users, regardless of their expertise in cryptography.
ACES focuses on researching and developing an open ML-enabled architecture to meet the growing demand for cloud services at the edge. The project ensures end-to-end transaction resilience, reliability, and stable automation in cloud management, while also maintaining secure flows of sensitive data and applications.
CROSSCON aims to create an open-source security stack for IoT, providing secure applications across diverse devices and architectures. It focuses on enhancing memory protection, isolating Trusted Execution Environments, and offering high-assurance trusted services with a lightweight security toolchain.
The goal of F-LION is to investigate techniques for robust distributed machine learning and developing a dynamic framework enabling public agencies to run a distributed learning scheme.