Publications

The rapid adoption of 5G networks, space networks, and Internet of Things (IoT) technologies in healthcare has significantly expanded the attack surface for cybersecurity threats. This evolving landscape demands robust defense mechanisms that can anticipate and neutralize sophisticated cyber-attacks. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies are pivotal in developing such advanced cybersecurity solutions. A critical factor influencing the effectiveness of these AI/ML models is the quality and diversity of the datasets used in their training. This paper presents a systematic review of various datasets used for AI/ML-based cybersecurity model training across multiple domains, with a focus on 5G networks, IoT healthcare, and space networks. By employing a structured Goal-Question-Metric (GQM) methodology and Quasi-Gold Standard (QGS) validation, we assessed the characteristics, applications, and limitations of real, synthetic, and hybrid datasets in enhancing cybersecurity measures. The review identifies key trends, gaps, and future research directions, highlighting the need for more diverse datasets, standardized benchmarks, and privacy-preserving techniques. Our findings offer insights into improving the resilience of AI/ML models for cybersecurity, guiding the development of more effective and adaptable defense strategies across emerging network technologies. 

Internet of Things (IoT) devices are becoming more and more common in homes, making the security and privacy of these increasingly important. To investigate how the users’ security and privacy practices can be improved, it is necessary to understand the current everyday practices and what impacts these. In an interview study, this paper reveals that users’ practices are primarily influenced by the convenience offered by the IoT device, and the motivation and the effort required from the user to make themselves aware of the security requirements and enact change within their IoT ecosystem.

Internet of Things (IoT) devices are becoming more and more common in homes, making the security and privacy of these increasingly important. To investigate how the users’ security and privacy practices can be improved, it is necessary to understand the current everyday practices and what impacts these. In an interview study, this paper reveals that users’ practices are primarily influenced by the convenience offered by the IoT device, and the motivation and the effort required from the user to make themselves aware of the security requirements and enact change within their IoT ecosystem.

This paper explores the enhancement of Science, Technology, Engineering, and Mathematics (STEM) education through the integration of network and systems security topics, and specifically hands-on, experimental learning activities. With the inclusion of real-world use cases of cybersecurity and trust topics into curricula, educators can adequately prepare their students for an era of a fully interconnected world. Strategies for curriculum design that can foster a diverse, competent workforce are discussed.

Among the various existing case scenarios within the contexts of DVI, there is also what has recently begun to be called MDVI (Migrant Disaster Victim Identification). This terminology groups together those scenarios characterised by the mass death of migrants during the migration process, including deaths caused by shipwrecks or drowning, violence, exposure to extreme environmental conditions, lack of health care and any other aetiology as long as they occur in transit or on the migratory route. In the last 10 years alone, more than 30,000 migrants have died trying to reach Europe (mostly in shipwrecks in the Mediterranean), of which more than 23,000 have not been identified.

More Info: https://migrant-dvi.eu/

In this perspective, we discuss the latest developments in machine-learning and deep-learning research on metal organic framework (MOF) materials and reflect on how their utilization has evolved within the large language models (LLMs) domain. We explore future benefits to accelerate and automate materials development research.

European forests  play a crucial role in  climate change mitigation and biodiversity conservation, though they are continuously adapting to rapid and continuous variations in environmental conditions. Ensuring their health and resilience requires timely detection of changes in forest status, functioning, and provided ecosystem services. However, accurate predictions of their future ecological, economic and social contributions depend on a well-coordinated approach that brings together ground-based forest inventory and monitoring networks, community science, and key stakeholders. 

A new paper published by Guerrieri and colleagues from the CLEANFOREST Cost Action core group highlights the urgent need for stronger synergies among these players. The authors advocate for a new era of forest monitoring and inventorying, where networks collaborate and coordinate their efforts to systematically track and assess the state and long-term changes of European forests. This can be achieved with the creation of an ‘alliance’ of forest monitoring and inventorying programs, which should fall under the auspices of international political bodies. The alliance could serve as the pan-European research infrastructure that centralizes discussion on protocols for data collections and data harmonization, priority needs for current and future monitoring and accessibility to the data from relevant end users, thus strengthening the European forest monitoring system.. The alliance is timely and essential to support the proposed EU Forest Monitoring Law, as well as other relevant European policy targets.

This Perspective paper is the result of debate during and after the panel discussion on ‘Building a common vision on forest monitoring amid global change: challenges and opportunities’ during the first annual meeting of the CLEANFOREST COST Action in Thessaloniki in 2023.

Plants, People, Planet is a multi-disciplinary Open Access journal, owned by the New Phytologist Foundation and published by Wiley. The journal publishes outstanding plant-based research in its broadest sense and celebrates everything new, innovative and exciting in plant-focused research that is relevant to society and people’s daily lives. The New Phytologist Foundation is an independent, not-for-profit organisation dedicated to the promotion of plant science.

In the past decade, metal-organic frameworks (MOFs) have been extensively researched for biomedical applications, particularly drug storage, after proving to be excellent substitutes for traditional porous materials. Biomedical applications of MOFs have been greatly expedited with the recent integration of data science and molecular modelling approaches to experimental research, establishing them as essential elements in medical imaging, diagnostics, and regenerative medicine. In this review, to demonstrate the potential of MOFs in cutting-edge treatments for neurological and cancer diseases, we examined the molecular interactions between MOFs and biological systems and discussed the field’s opportunities and challenges.

The Internet of Things (IoT) is transforming industries with its plethora of applications, from smart cities to healthcare. However, this rapid proliferation comes with a pressing need for additional cybersecurity needs to ensure stable and resilient operations. Resource-constrained IoT devices often rely on basic cryptographic mechanisms, allowing innovative solutions like Radio Frequency Fingerprinting (RFF) to thrive and enhance physical layer security. As part of European Union COST Action, this study dives into the current state-of-the-art in RFF, a technique that utilizes unique hardware characteristics for device authentication and classification.  Key Takeaways:  Challenges in Real-World Deployment: While RFF has shown promise in academic settings, its practical applications are limited by technological barriers.   There is a burgeoning need to deliberate the potential CONOPS (concept of operation) and operational scenarios encountered in real world deployment. The research sheds light on critical performance metrics and the complexities of evaluating RFF systems.  By addressing these challenges, the study lays a foundation for advancing RFF from theory and controlled environment to practical implementation, enabling its integration into secure IoT ecosystems.  By connecting this work to the COST action, we aim to foster collaboration across Europe to develop cutting-edge security solutions for networks of the future.