In the fast-progressing field of wireless communications, the forthcoming 6G networks are expected to revolutionize the way we communicate, offering unparalleled speed, minimal latency, and seamless connectivity. However, amid this evolution, the paramount concern remains the security and privacy of the data traversing these networks. Traditional centralized artificial intelligence (AI) techniques already struggle to keep up with the vast amount of data of future 6G networks and deal with the increasing worries about privacy. Federated learning (FL), emerges as a key enabler of Trustworthy AI (TAI), empowering the engagement of distributed network nodes in AI training without the need for exchanging raw data, thereby mitigating the risks associated with centralized data processing. In this paper, we provide a comprehensive survey on the potential of FL in enhancing the security of 6G networks. Particularly, we begin by providing the necessary background on 5G networks and FL, setting the stage for understanding their current and future implications. We then explore the current state-of-the-art of FL applications within 5G networks and their relevance to the future threat landscape of 6G. Subsequently, we examine the inherent vulnerabilities of FL systems, major attacks against FL in the context of 5G networks, and corresponding defense mechanisms. Finally, we discuss the integration of advanced FL technologies and concepts towards enhanced cybersecurity and privacy in 6G networks, aiming to cover all aspects and future perspectives of FL within the context of the forthcoming 6G threat landscape.

Electronic health records (EHR) are patient-level information, e.g., laboratory tests and questionnaires, stored in electronic format. Compared to physical records, the EHR alternative allows patients to access their data easily and helps staff with management procedural tasks such as information sharing across different organizations. Moreover, this type of data is commonly used by researchers for predictive and classification purposes, employing statistical and machine learning methods. However, missingness is a phenomenon that is observed very frequently for such measurements. Even though this missingness is often significant, it is usually treated poorly with either case deletion or simple methods, resulting in suboptimal and/or inaccurate predictive results. This happens because the simple methods, e.g., k-nearest neighbors (kNN) and mean/mode imputation, fail in most cases to incorporate the complex relationships that define these medical datasets. To address these limitations, in this paper we test and improve state-of-the-art missing data imputation models and practices. We propose a new missing value imputation method based on denoising autoencoders (DAE) with kNN for the pre-imputation task. We optimize the training methodology by re-applying kNN to the missing data every $N$ epochs using a different value for the variable $k$ each time to yield more accurate results. We also revise a state-of-the-art missing data imputation approach based on a generative adversarial network (GAN). Using this as a baseline, we introduce improvements regarding both the architecture and the training procedure. These models are compared with the ones usually employed within clinical research studies for both the task of imputation and post-imputation prediction. Results show that our proposed deep learning approaches outperform the standard baselines, yielding better imputation and predictive results.

Recent studies report that cybersecurity breaches noticed in hospitals are associated with low levels of personnel’s cybersecurity awareness. This work aims to assess the cybersecurity culture in healthcare institutions from middle- to low-income EU countries. The evaluation process was designed and performed via anonymous online surveys targeting individually ICT (internet and communication technology) departments and healthcare professionals. The study was conducted in 2019 for a health region in Greece, with a significant number of hospitals and health centers, a large hospital in Portugal, and a medical clinic in Romania, with 53.6% and 6.71% response rates for the ICT and healthcare professionals, respectively. Its findings indicate the necessity of establishing individual cybersecurity departments to monitor assets and attitudes while underlying the importance of continuous security awareness training programs. The analysis of our results assists in comprehending the countermeasures, which have been implemented in the healthcare institutions, and consequently enhancing cybersecurity defense, while reducing the risk surface.

The coronavirus pandemic led to an unprecedented crisis affecting all aspects of the concurrent reality. Its consequences vary from political and societal to technical and economic. These side effects provided fertile ground for a noticeable cyber-crime increase targeting critical infrastructures and, more specifically, the health sector; the domain suffering the most during the pandemic. This paper aims to assess the cybersecurity culture readiness of hospitals’ workforce during the COVID-19 crisis. Towards that end, a cybersecurity awareness webinar was held in December 2020 targeting Greek Healthcare Institutions. Concepts of cybersecurity policies, standards, best practices, and solutions were addressed. Its effectiveness was evaluated via a two-step procedure. Firstly, an anonymous questionnaire was distributed at the end of the webinar and voluntarily answered by attendees to assess the comprehension level of the presented cybersecurity aspects. Secondly, a post-evaluation phishing campaign was conducted approximately four months after the webinar, addressing non-medical employees. The main goal was to identify security awareness weaknesses and assist in drafting targeted assessment campaigns specifically tailored to the health domain needs. This paper analyses in detail the results of the aforementioned approaches while also outlining the lessons learned along with the future scientific routes deriving from this research.

The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAl) within the European Union (EU), with a specific focus on the energy sector. The analysis encompasses legal frameworks, directives, initiatives, and standards like the AI Ethics Guidelines for Trustworthy AI (EGTAI), the Assessment List for Trustworthy AI (ALTAI), the AI act, and relevant CEN-CENELEC standardization efforts, as well as EU-funded projects such as AI4EU and SHERPA. Subsequently, we introduce a new TAl application framework, called E-TAl, tailored for energy applications, including smart grid and smart building systems. This framework draws inspi-ration from EGTAI but is customized for AI systems in the energy domain. It is designed for stakeholders in electrical power and energy systems (EPES), including researchers, developers, and energy experts linked to transmission system operators, distribution system operators, utilities, and aggregators. These stakeholders can utilize E-TAl to develop and evaluate AI services for the energy sector with a focus on ensuring trustworthiness throughout their development and iterative assessment processes.

The need for centralized e-Government strategies and policies is a fertile ground for the development of e-Government INTEROPERABILITY FRAMEWORKS (eGIFs). Integrated information systems and one-stop public services to citizens and businesses are heavily dependent on the resolution of the ever-expanding interoperability challenge and the satisfaction of public services' stakeholders. This can only be realistically addressed if the scope of eGIFs is widened, to include service composition and discovery, security standards, certification of public sites, as well as unified data models for achieving a common understanding of semantics at a syntax-independent level. The Greek eGIF presents a transition from paper-based specifications to a robust system that acts as a repository of systems, services, data schemas, certification tools and training material. This paper presents the objectives, the overall methodology and the architecture of the Greek eGIF, that meets today's demands for modelling, storing, managing and updating a vast number of eGovernment-related information.