The increasing interconnection of technology in healthcare between devices at the physical and cyber levels has transformed these infrastructures into large Health Care Information Infrastructures. Such HCIIs are considered critical and sensitive infrastructures due to their importance for people’s well-being and safety. On the other hand, the evolving digital interconnectivity has also changed the threat landscape, producing a wide range of security and privacy challenges and increasing the danger of potential cybersecurity attacks. The integrated nature introduces new potential entry points for cybersecurity risks. Thus, there is an urgent, pressing need for the Health operators to protect their HCIIs. Efficient situational awareness, incident handling and risk assessment is an important step to acquiring a thorough and common understanding of cyber-attack situations, and is necessary to timely reveal security events and data breaches occurring into HCIIs. Consequently, analysis of incident information is crucial in attempting to detect the presence of a threat, within HCIIs, that has already been detected in other interdependent systems within the same ecosystem. AI4HEALTHSEC proposes a state of the art solution that improves the detection and analysis of cyber-attacks and threats on HCIIs, and increases the knowledge on the current cyber security and privacy risks. Additionally, AI4HEALTHSEC builds risk awareness, within the digital Healthcare ecosystem and among the involved Health operators, to enhance their insight into their Healthcare ICT infrastructures and provides them with capability to react in case of security and privacy breaches. Last but not least AI4HEALTHSEC fosters the exchange of reliable and trusted incident-related information, among ICT systems and entities composing the HCIIs without revealing sensitive corporate details

Phoenix aims to develop a fundamentally novel computational model for reconstructing complex software systems, following some massive internal failure or external infrastructure damage. Recovering system operations is a challenging problem as it may require excessive system reconstruction using a different infrastructure (i.e., computational and communication devices named system cells) from the one that the system was originally designed for. Thus, software functionality may have to be remodularised and allocated onto devices with very different characteristics than the ones originally used but with some generic capabilities. Phoenix aims to develop a bio-inspired paradigm for reconstructing nearly extinct complex software systems based on a novel computational DNA (co-DNA) oriented systems modelling approach. The co-DNA will encapsulate logic and program code and will enable the use of analogues of biological processes for transmitting, transforming, combining, activating and deactivating it across computational and communication devices. The purpose of encoding the co-DNA of a system, and computational analogues of biological processes using it, is to enable other computational devices receiving the co-DNA to act as parts of the system that needs to be reconstructed, realise chunks of its functionality, and spread further the system reconstruction process. The Phoenix approach will bring a breakthrough in the current software system design and engineering paradigm. This will be through, not only a fundamentally new way of engineering mechanisms to support the resilience, continuity and recovery of software systems, but also the initiation of a new paradigm of designing and implementing software systems, based on the encoding of a system co-DNA that can trigger processes of self-regulated and incrementally expanding system functionality.

The latest cancer statistics highlight encouraging advances in decreasing cancer-related mortality. However, given that one in two people will be diagnosed with cancer in their lifetime, and due to the growing and ageing population, the absolute number of people living with cancer is set to keep increasing substantially in the near future. The main objective of ASCAPE is to take advantage of the recent ICT advances in Big Data, Artificial Intelligence and Machine Learning to support cancer patients’ quality of life and health status. To achieve its objective, ASCAPE will create an open AI infrastructure that will enable health stakeholders (hospitals, research institutions, companies, etc.) to deploy and execute its AI algorithms locally on their private data. Any new knowledge produced by this process will be sent back to the open AI infrastructure. This way the knowledge will be shared among everyone while the medical data will still remain private. The services to be designed, piloted and deployed inside this project will include intelligent interventions for physiological and psychological support, improved patient and family counselling and guidance, early diagnosis and forecasts of ill-health, identification of disease trajectories and relapse, improved health literacy etc. ASCAPE will focus the training of the AI in two types of cancer, breast and prostate. This way, it will achieve sufficient coverage across genders as well as age groups, hence facilitating its ongoing improvements and applicability towards any type of cancer in the future. The ASCAPE project will be developed in 36 months by a competitive consortium of 15 partners from 7 countries, which corresponds to a well-balanced structure, involving big companies, SMEs, research centres and universities. Despite the great diversity of entities within the proposal, ASCAPE partners bring state-of-the-art complementary skills ensuring the ability of the consortium to develop the proposed solutions.

Recent trends in industrial technology and the adaptation of Industrial Internet of Things (ΙIοΤ), has emerged by the convergence of Operations Technology (i.e., traditional hardware and software systems) and Information Technology (i.e., advanced computing, data aggregation/analysis, and ubiquitous communication systems). IIoT has great potential to enable significant advances in optimizing operations among large number of increasingly autonomous control systems and devices, and can have a profound impact on many industry domains, where smart factories and logistics are among most notable cases. However, a major barrier towards IIoT adoption lies in cybersecurity issues that makes it extremely difficult to harness its full potential: IIoT systems dramatically increase the attack surface (introducing new security threats due to newly connected devices and protocols, making them more vulnerable to interference), the disruption of process controls, the theft of intellectual property, the loss of corporate data, and the industrial espionage. C4IIoT will build and demonstrate a novel and unified IIoT cybersecurity framework for malicious and anomalous behavior anticipation, detection, mitigation, and end-user informing. The framework provides a holistic and disruptive security-enabling solution for minimizing attack surfaces in IIoT systems, by exploiting i) emerging security software and hardware protection mechanisms; ii) state of the art machine and deep learning and privacy-aware analytics; iii) novel encrypted network flow analysis; iv) secure-by-design IIoT device fabrication; and v) blockchain technologies, to provide a viable scheme for enabling security and accountability, preserving privacy, enabling reliability and assuring trustworthiness within IIoT applications. The C4IIoT framework will be demonstrated and validated on two carefully selected use cases in real world environments, namely Enabling security IIoT in i) Inbound Logistics and ii) a Smart Factory

SEMIoTICS aims to develop a pattern-driven framework, built upon existing IoT platforms, to enable and guarantee secure and dependable actuation and semi-autonomic behaviour in IoT/IIoT applications. Patterns will encode proven dependencies between security, privacy, dependability and interoperability (SPDI) properties of individual smart objects and corresponding properties of orchestrations involving them. The SEMIoTICS framework will support cross-layer intelligent dynamic adaptation, including heterogeneous smart objects, networks and clouds, addressing effective adaptation and autonomic behaviour at field (edge) and infrastructure (backend) layers based on intelligent analysis and learning. To address the complexity and scalability needs within horizontal and vertical domains, SEMIoTICS will develop and integrate smart programmable networking and semantic interoperability mechanisms. The practicality of the above approach will be validated using three diverse usage scenarios in the areas of renewable energy (addressing IIoT), healthcare (focusing on human-centric IoT), and smart sensing (covering both IIoT and IoT); and will be offered through an open API. SEMIoTICS consortium consists of strong European industry (Siemens, Engineering, STMicroelectronics), innovative SMEs (Sphynx, Iquadrat, BlueSoft) and academic partners (FORTH, Uni Passau, CTTC) covering the whole value chain of IoT, local embedded analytics and their programmable connectivity to the cloud IoT platforms with associated security and privacy. The consortium is striving for a common vision of creating EU’s technological capability of innovative IoT landscape both at European and international level.