COCOON OBJECTIVES

I. Enhanced Information Exchange: Facilitate secure data processing and enhance real-time information exchange among Energy and Power Systems (EPES) stakeholders. Achievement assessed through KPIs such as response rate and latency. 

II. Effective Early Warning System: Establish a practical Early Warning System (EWS) utilizing advanced technologies for precise measurement and Deep Learning for exploit detection. Timely updates aligned with European Union Cyber Security Incident Response Team (EU CSIRT) metrics.

III. Real-time Cyber-Physical Protection: Align Operational Technology (OT) properties with Information Technology (IT) vulnerabilities for grid stability. Achieve high accuracy in vulnerability assessment by correlating cyber threat vectors with OT components.

IV. Data-driven Detection of Exploits: Utilize graph-based data provenance and Deep Learning for detecting known and zero-day exploits. Construct attack vector paths by correlating with Open Source Intelligence (OSINT) and Cyber Threat Intelligence (CTI) feeds. Track accuracy using threat regression profiling metrics.

V. Strengthen Resilience in Grid Stability: Orchestrate threat mitigation through the programmable dataplane in the Cocoon Programmable Node (CPN), employing Deep Reinforcement Learning (DRL) for optimal operation during attacks. Converge network infrastructure under large-scale attacks, examining scenarios and tracking with KPIs.

IMPEMENTATION STRATEGIES:

• Develop a sophisticated Programmable Node, the CPN, to accelerate data processing, forwarding, and control functionalities. This forms the foundation for a bottom-up solution in cyber protection applications.
• Integrate an Early Warning System (EWS) that cooperatively utilizes cyber-physical protection mechanisms and operator training. The system, complemented by CPN functionalities, ensures real-time measurement, monitoring, and DL-based attack diagnosis.
• Employ a measurement-based methodology to map explicit OT properties to IT vulnerabilities. Address challenges arising from the convergence of IT with OT technologies.
• Achieve data-driven detection of known and unknown exploits by synergizing graph-based data provenance and DL-based regression analysis. Ensure a robust cybersecurity posture by correlating with OSINT and CTI feeds.
• Implement practical network and system threat mitigation mechanisms orchestrated by the cross-domain programmable data plane scheme within the CPN. Fortify the resilience of interactions among entities involved in grid stability processes.