SHAPES Marketplace: Transparency, Trust and Fair Competition in the Healthy Ageing Market.

Access to digital health and care solutions and services that promote healthy ageing, independent living, and ageing in place is limited due to significant market barriers and challenges. The SHAPES project addresses the challenge of ageing populations by developing a sociotechnical ecosystem comprising a variety of health and care digital solutions, tools and services to enable and facilitate active, independent, and healthy ageing at home. Within the SHAPES project, the SHAPES Marketplace serves as a one-stop-shop for digital solutions and services designed for the Silver Economy that target the smart and healthy ageing and independent living markets. Delivering a dynamic catalogue of health and care digital solutions and services, the Marketplace promotes a transparent expansion of a trusted market offer on digital solutions and services for healthy ageing and independent living on a pan-European scale, thereby preventing vendor lock-in and enhancing the agile and fair competitiveness of the health and care industry, particularly in Europe. This paper introduces the SHAPES Marketplace and considers its function as a market driver to raise awareness on the benefits and impact of health and care digital solutions and services, as well as to shape the healthy ageing market, upholding the Systems-Market for Assistive and Related Technologies (SMART) Thinking Matrix to stimulate transparency, trust and fair competition.

Towards a Machine Learning Based Situational Awareness Framework for Cybersecurity: An SDN Implementation.

The ever-increasing number of internet-connected devices, along with the continuous evolution of cyber-attacks, in terms of volume and ingenuity, has led to a widened cyber-threat landscape, rendering infrastructures prone to malicious attacks. Towards addressing systems' vulnerabilities and alleviating the impact of these threats, this paper presents a machine learning based situational awareness framework that detects existing and newly introduced network-enabled entities, utilizing the real-time awareness feature provided by the SDN paradigm, assesses them against known vulnerabilities, and assigns them to a connectivity-appropriate network slice. The assessed entities are continuously monitored by an ML-based IDS, which is trained with an enhanced dataset. Our endeavor aims to demonstrate that a neural network, trained with heterogeneous data stemming from the operational environment (common vulnerability enumeration IDs that correlate attacks with existing vulnerabilities), can achieve more accurate prediction rates than a conventional one, thus addressing some aspects of the situational awareness paradigm. The proposed framework was evaluated within a real-life environment and the results revealed an increase of more than 4% in the overall prediction accuracy.