Artificial intelligence (AI) has lately proved to be a coin with two sides. On the one hand, it can be leveraged as a powerful defensive mechanism to improve system preparedness and response against cyber incidents and attacks, and on the other hand, it can be a formidable weapon attackers can use to damage, compromise or manipulate systems. AI4CYBER ambitions to provide an Ecosystem Framework of next-generation trustworthy cybersecurity services that leverage AI and Big Data technologies to support system developers and operators in effectively managing robustness, resilience, and dynamic response against advanced and AI-powered cyberattacks. The project will deliver a new breed of AI-driven software robustness and security testing services that significantly facilitates the testing experts work, through smarter flaw identification and code fixing automation. Moreover, the project will provide cybersecurity services for comprehension, detection and analysis of AI-powered attacks to prepare the critical systems to be resilient against them. Incident response support by AI4CYBER will offload security operators from complex and tedious tasks offering them mechanisms to optimize the orchestration of the most appropriate combination of security protections, and continuously learn from system status and defences’ efficiency. The AI4CYBER framework will ensure fundamental rights and values-based AI technology in its services, through the integration of demonstrable explainability, fairness and technology robustness (security) capabilities in the AI4CYBER components. The ecosystem will be validated in three scenarios: i) Detection and Mitigation of AI-powered Attacks against the Energy Sector, ii) Robustness and autonomous adaptation of Banking applications to face AI-powered attacks and iii) Resilient hospital services against advanced and AI-powered cyber-physical attacks.

The European energy system is undergoing a significant transformation: decarbonization, security of supply, deployment of renewables and their integration into the market, generating significant opportunities and challenges for energy stakeholders. Despite all energy efficiency efforts, overall demand for decarbonized electricity is set to be significantly higher in 2050 than today due to the decarbonization of the heating, cooling, transport and many industrial sectors, which can only be achieved via efficient and smart electrification. Hydropower is a key technology in supporting the European pathway to a decarbonized energy system and to achieve global leadership in renewable energy generation. It consists a renewable and highly sustainable electricity resource and can supply the European power system with stability and valuable flexibility. In addition, hydropower reduces EU’s dependency on fossil imports and renders multiple extra benefits for society in the river basins such as support to irrigation, water supply and flood control. The D-HYDROFLEX project will advance excellence in research on digital technology for hydropower paving the way towards more efficient, more sustainable, and more competitive hydropower plants in modern power markets. D-HYDROFLEX will develop a toolkit for digitally ‘renovating’ the existing hydroelectric power plants based on sensors, digital twins, AI algorithms, hybridization modelling (power-to-hydrogen), cloud-edge computing and image processing. The core pillars of the project will be: (i) digitalization, (i.e., digital twins for hydro dams and machinery, weather and flow forecasts, cyber-resilience), (ii) flexibility, (i.e., coordination with hydrogen, storage and VPP operation) and (iii) sustainability, (i.e., biodiversity environmental issues). Validation will take place in real hydro plants of EDF (France), TEE (Poland), PPC (Greece), TASGA (Spain) and INTEX (Romania), covering different geographical areas of Europe.

The overall aim of NANCY is to introduce a secure and intelligent architecture for the beyond the fifth generation (B5G) wireless network. Leveraging AI and blockchain, NANCY enables secure and intelligent resource management, flexible networking, and orchestration. In this direction, novel architectures, namely point-to-point (P2P) connectivity for device-to-device connectivity, mesh networking, and relay-based communications, as well as protocols for medium access, mobility management, and resource allocation will be designed. These architectures and protocols will make the most by jointly optimizing the midhaul, and fronthaul. This is expected to enable truly distributed intelligence and transform the network to a low-power computer. Likewise, by following a holistic optimization approach and leveraging the developments in blockchain, NANCY aims at supporting E2E personalized, multi-tenant and perpetual protection.

Improving the CI capacities at preparedness, detection and response phases requires the attention to the human factor as well as the collaboration of heterogeneous organisations involved in the CI development and operation, ensuring a continuum of care, just as it is done for other ICT systems with the adoption of SecDevOps approaches. DYNABIC Consortium believes that the adoption of defensive AI and novel approaches to continuous business risk management based on enhanced SecDevOps can drastically improve critical services resilience. Furthermore, AI-based self-healing and autonomous response automation can greatly help to achieve fast and efficient recovery and enable the provision of fully resilient critical services to European citizens. The strategic objective of DYNABIC is to increase the resilience and business continuity capabilities of European critical services in the face of advanced cyber-physical threats. This objective will be pursued by delivering new socio-technical methods, models and tools to support resilience through holistic business continuity risk management and control in operation, and dynamic adaptation of responses at system, human and organization planes. DYNABIC will deliver the DYNABIC Framework that will enable OES to predict, quantitatively assess and mitigate in real-time business continuity risks and their potential cascading effects. Furthermore, it will enable the dynamic autonomous adaptation of critical infrastructures to meet Resilience goals by the automatic optimization and orchestration of response strategies. The DYNABIC framework will be validated in two types of demonstrations: i) Smart Preparedness, prevention and Response to Business Disruption risks in 4 critical infrastructures and supply chains (EV charging stations, Critical transport services, Telco services, and Hospital services), and i) Smart Preparedness and Response to Cascading Business Disruption risks in interconnected CIs.

Next-generation industrial systems promise to deliver unprecedented excellence not only in terms of performance, but also explainability, trustworthiness and transparency. To achieve this new objectives, state-of-the-art concepts of artificial intelligence (AI), edge-to-cloud (E2C) computing, blockchain, and visualisation need to be de-risked and applied. Motivated by this, TALON aims at sculpturing the road towards the next Industrial revolution by developing a fully-automated AI architecture capable of bringing intelligence near the edge in a flexible, adaptable, explainable, energy and data efficient manner. TALON architecture consists of three fundamental pillars: a) an AI orchestrator that coordinates the network and service orchestrators in order to optimise the edge vs cloud relationship, while boosting reusability of datasets, algorithms and models by deciding where each one should be placed; b) a lightweight hierarchical blockchain schemes that introduce new service models and applications under a privacy and security umbrella; and c) new digital-twin empowered transfer learning and visualization approaches that enhance AI trustworthiness and transparency. It combines the benefits of AI, edge and cloud networking, as well as blockchain and DTs, optimized by means of a) new key performance indicators that translate the AI benefits into insightful metrics; b) novel theoretical framework for the characterisation of the AI; c) blockchain used to deliver personalised & perpetual protection based on security, privacy and trust mechanisms; d) AI approaches for automatically and co-optimising edge and cloud resources as well as the AI execution nodes; e) semantic AI to reduce the learning latency and enhance reusability; and f) digital twins that visualize the AI outputs and together with human-in-the-loop approaches. All the technological breakthroughs are demonstrated, validated and evaluated by means of proof-of-concept simulation and four real-world pilots.