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PHOENIX

Key Facts

  • Title: Electrical Power System’s Shield against complex incidents and extensive cyber and privacy attacks
  • GA Number: 832989
  • Start date: 01/09/2019
  • End date: 31/08/2022
  • Contact: phoenix-info.fr (at) capgemini.com
  • Phoenix
  • H2020phoenix
  • phoenix-h2020

Objectives

PHOENIX will realize 3 strategic goals:

  • Strengthen EPES cybersecurity preparedness by employing a) “security by design” via novel
    protective concepts for resilience, self-healing and accountability, and b) “security by innovation” via
    adapting, upgrading, integrating and validating a number of TRL5 developments to TRL7-8.
  • Coordinate EPES cyber incident discovery, response and recovery, contributing to the
    implementation of the NIS Directive by developing and validating at national and pan-European level,
    a novel fully decentralized near real-time synchronized information awareness exchange platform,
    among authorized utilities, CSIRTs, ISACs, CERTs, NRAs and the strategic NIS cooperation group
  • Accelerate research and innovation in EPES cybersecurity by a novel prevent, detect and mitigate
    DevSecOps mechanism, secure and privacy preserving federated Machine Learning (ML) algorithms
    and definition of certification methodologies and procedures

Pilots

PHOENIX will involve real-world scenarios to validate the effectiveness of PHOENIX across 5 European Large Scale Pilots (LSP) in Italy, Germany, Slovenia, Greece and Romania involving the completeend-to-end generation, transmission, distribution and presumption value chain. Beyond the individual LSPs, cascading effects even to other critical infrastructures will be simulated and cross-border security and privacy sites will be tested and validated.

Scope

PHOENIX implements innovative technological solutions such as Universal Secure Gateway (USG), Secure & Persistent Communications (SPC), utilizing blockchains, inter-DLTs and SDN/5G technologies (WP3). Situation Awareness & Early-Stage Detection is achieved via a GPU assisted ML framework and privacy preserving, secure federated ML training, which protect sensitive and confidential training data sets. Smooth collaboration between utilities, EPES stakeholders and CERTs for the realization of NIS Directive is implemented via a cooperative, fully distributed I2SP platform