Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focussing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we validate it on three use cases from real-world case studies.

Addressing cyber and privacy risks has never been more critical for organisations. While a number of risk assessment methodologies and software tools are available, it is most often the case that one must, at least, integrate them into a holistic approach that combines several appropriate risk sources as input to risk mitigation tools. In addition, cyber risk assessment primarily investigates cyber risks as the consequence of vulnerabilities and threats that threaten assets of the investigated infrastructure. In fact, cyber risk assessment is decoupled from privacy impact assessment, which aims to detect privacy-specific threats and assess the degree of compliance with data protection legislation. Furthermore, a Privacy Impact Assessment (PIA) is conducted in a proactive manner during the design phase of a system, combining processing activities and their inter-dependencies with assets, vulnerabilities, real-time threats and Personally Identifiable Information (PII) that may occur during the dynamic life-cycle of systems. In this paper, we propose a cyber and privacy risk management toolkit, called AMBIENT (Automated Cyber and Privacy Risk Management Toolkit) that addresses the above challenges by implementing and integrating three distinct software tools. AMBIENT not only assesses cyber and privacy risks in a thorough and automated manner but it also offers decision-support capabilities, to recommend optimal safeguards using the well-known repository of the Center for Internet Security (CIS) Controls. To the best of our knowledge, AMBIENT is the first toolkit in the academic literature that brings together the aforementioned capabilities. To demonstrate its use, we have created a case scenario based on information about cyber attacks we have received from a healthcare organisation, as a reference sector that faces critical cyber and privacy threats.

The technological leap of smart technologies and the Internet of Things has advanced the conventional model of the electrical power and energy systems into a new digital era, widely known as the Smart Grid. The advent of Smart Grids provides multiple benefits, such as self-monitoring, self-healing and pervasive control. However, it also raises crucial cybersecurity and privacy concerns that can lead to devastating consequences, including cascading effects with other critical infrastructures or even fatal accidents. This paper introduces a novel architecture, which will increase the Smart Grid resiliency, taking full advantage of the Software-Defined Networking (SDN) technology. The proposed architecture called SDN-microSENSE architecture consists of three main tiers: (a) Risk assessment, (b) intrusion detection and correlation and (c) self-healing. The first tier is responsible for evaluating dynamically the risk level of each Smart Grid asset. The second tier undertakes to detect and correlate security events and, finally, the last tier mitigates the potential threats, ensuring in parallel the normal operation of the Smart Grid. It is noteworthy that all tiers of the SDN-microSENSE architecture interact with the SDN controller either for detecting or mitigating intrusions.

Since their appearance at the dawn of the second millennium, Delay or Disruption Tolerant Networks (DTNs) have gradually evolved, spurring the development of a variety of methods and protocols for making them more secure and resilient. In this context, perhaps, the most challenging problem to deal with is that of cryptographic key management. To the best of our knowledge, the work at hand is the first to survey the relevant literature and classify the various so far proposed key management approaches in such a restricted and harsh environment. Towards this goal, we have grouped the surveyed key management methods into three major categories depending on whether the particular method copes with (a) security initialization, (b) key establishment, and (c) key revocation. We have attempted to provide a concise but fairly complete evaluation of the proposed up-to-date methods in a generalized way with the aim of offering a central reference point for future research.

As the upsurge of information and communication technologies has become the foundation of all modern application domains, fueled by the unprecedented amount of data being processed and exchanged, besides security concerns, there are also pressing privacy considerations that come into play. Compounding this issue, there is currently a documented gap between the cybersecurity and privacy risk assessment (RA) avenues, which are treated as distinct management processes and capitalise on rather rigid and make-like approaches. In this paper, we aim to combine the best of both worlds by proposing the APSIA (Automated Privacy and Security Impact Assessment) methodology, which stands for Automated Privacy and Security Impact Assessment. APSIA is powered by the use of interdependency graph models and data processing flows used to create a digital reflection of the cyber-physical environment of an organisation. Along with this model, we present a novel and extensible privacy risk scoring system for quantifying the privacy impact triggered by the identified vulnerabilities of the ICT infrastructure of an organisation. We provide a prototype implementation and demonstrate its applicability and efficacy through a specific case study in the context of a heavily regulated sector (i.e., assistive healthcare domain) where strict security and privacy considerations are not only expected but mandated so as to better showcase the beneficial characteristics of APSIA. Our approach can complement any existing security-based RA tool and provide the means to conduct an enhanced, dynamic and generic assessment as an integral part of an iterative and unified risk assessment process on-the-fly. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that such holistic security and privacy mechanisms can reach their full potential towards solving this conundrum.

In the past two decades, the world has experienced an unprecedented number of extreme weather events, some causing major human suffering and economic damage. The March 2012 heat wave is one of the most known and broadly discussed events in the Northeast United States (NE-USA). The present study examines in depth the possible influence of solar activity on the historic March 2012 heat wave based on a comparison of solar/space and meteorological data. Our research suggests that the historic March 2012 heat wave (M2012HW) and the March 1910 heat wave (M1910HW), which occurred a century earlier in NE-USA, were related to Sun-generated special space plasma structures triggering large magnetic storms. Furthermore, the largest (Dst = −222 nT) magnetic storm during solar cycle 24 in March 2015 (only three years later than the March 2012 events) occurred in relation to another heat wave (M2015HW) in NE-USA. Both these heat waves, M2012HW and M2015HW, resemble each other in many ways: they were characterized by extremely huge temperature increases ΔΤ_Μ = 30° and 32° (with maximum temperatures Τ_Μ = 28° and 23°, respectively) during a positive North Atlantic Oscillation index, the high temperatures coincided with large-scale warm air streaming from southern latitudes, they were accompanied by superstorms caused by unexpected geoeffective interplanetary coronal mass ejections (ICMEs), and the ICME-related solar energetic particle (SEP) events were characterized by a proton spectrum extending to very high (>0.5 GeV) energies. We infer that (i) all three heat waves examined (M2012HW, M2015HW, M1910HW) were related with strong magnetic storms triggered by effective solar wind plasma structures, and (b) the heat wave in March 2012 and the related solar activity was not an accidental coincidence; that is, the M2012HW was most probably affected by solar activity. Future case and statistical studies are needed to further check the hypothesis put forward here, which might improve atmospheric models in helping people’s safety, health and life.