A Hardware and Software system, as a secure network edge device, direct connected with existing EPES assets (i.e. RTUs, PLC, SCADA).
Αn innovative and high-performance GPU based machine deep learning (ML) framework for offline and online/real-time processing and situation awareness.
Deep-learning methodologies, big-data analysis and proposing countermeasures, leading to significant reduction of security costs.
The PHOENIX approach focuses on the protection of the European end-to-end EPES (Electrical Power and Energy System) (from energy production to prosumption) via prevention, early detection and fast mitigation of cyber-attacks against EPES assets and networks and from (intentional and unintentional, internal and external) human activities, while protecting the utilities and end-users’ privacy from data breaches by design.
A distributed yet fully synchronized pan-European I2SP which will collect and share incidents’ information and trained ML models without the need to share sensitive information across EPES operators and CERTs.
Self-learning mechanism aimed at cyber-human incidents/attacks/accidents mitigation, prioritizing the most relevant information.
Implementation of an advanced legal framework for suitably managing data, ensuring adequate levels of GDPR compliance, well beyond legacy Data Management Platforms.
Created to accelerate the testing and certification of new EPES secure products in the market.