The Internet of Things (IoT)-enabled smart healthcare systems improve quality of life (QoL) but face more threats leading to an increase in abuse of the system. A key security technique is four-step threat modelling during system design, based on the four-layer IoT reference model. The objective of this article is to provide a review of studies published in the period 2014–2025 utilizing Google Scholar, IEEE Xplore and Web of Science. The search was conducted using the following keyword combinations: (threat modelling OR threat analysis) AND (Internet of Things) AND (smart healthcare). The review is based on 19 studies dealing with practical threat modelling or analysis of smart IoT healthcare systems. The reviewed studies reveal that threat modelling is rarely subjected to systematic validation, leaving it largely theoretical rather than practical. This recurring pattern highlights a methodological limitation that reduces the applicability and impact of current research. Moreover, they show that current research seldom addresses higher-level, context-rich layers where privacy, safety and QoL impacts are most significant. This consistent focus on lower layers suggests a systemic limitation, as threats propagate across multiple levels. Lack of automation, reliance on new technologies and no standardized methodology may explain why many studies fail to cover all security layers and threat modelling steps. To tackle IoT-enabled smart healthcare security issues, four solutions are proposed: (i) embedding thorough threat modelling into healthcare policies, (ii) performing comprehensive four-step threat modelling for IoT healthcare systems, (iii) developing a standardized approach and (iv) fostering automated, industry-specific threat modelling frameworks.

Abstract The covid-19 pandemic has motivated the cyber security research activities mainly at national and organizational level, but broader assessment of cyber security at individual level is undoubtedly lacking. Cyber security at individual level refers to humans and their personal cyber security practices that is called cyber hygiene. This study aims to evaluate the impact of the covid-19 pandemic circumstances on cyber hygiene outcomes, i.e., awareness, behavior, and knowledge. The intention was to estimate and compare the cyber hygiene of university students prior to and during the covid-19 pandemic. The results of the survey study indicate that covid-19 pandemic circumstances have caused the change of level of cyber hygiene awareness, behavior, and knowledge of university students. The covid-19 pandemic has improved all these cyber hygiene outcomes and that could be the consequence of intense use of digital space and mirroring of strict covid-19 pandemic protection measures. However, it is evident trend of cyber hygiene knowledge decline which coincides with covid-19 pandemic protection measures relaxation and less utilization of digital services. The findings of this study can be used to improve cyber hygiene worldwide and create a better extraordinary event response as digital space is expected to be increasingly used in future extraordinary circumstances.

Quality of experience (QoE) is a multidimensional concept that has been in the focus of the research community for quite some time, especially after the rapid development of information and communication technology (ICT). It has been examined in many contexts (web, telecommunications, economy, medicine, etc.), for different media types (text, audio, video, augmented/virtual reality, etc.) and systems in various domains. Given that logistics and transport is a potent ICT-based domain for research activity in the future, in order to ease and improve the investigation of QoE in the contexts of logistics and transport, we aim to sum the recommendations and lessons learned for the investigation of QoE on the basis of our existing research. These suggestions cover how to treat the concept of QoE and relate it to other fields, techniques and environments to examine and treat QoE, as well as future technologies that need to be included. We provide insight into how these challenges can affect examination of QoE in logistics and transport and discuss ways to overcome them.

Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE prediction. As a consequence, dimensionality issues occur in QoE prediction models. This paper gives an overview of the used dimensionality reduction technique in QoE modeling and proposes modification and use of Active Subspaces Method (ASM) for dimensionality reduction. Proposed modified ASM (mASM) uses variance/standard deviation as a measure of function variability. A straightforward benefit of proposed modification is the possibility of its application in cases when discrete or categorical IFs are included. Application of modified ASM is not restricted to QoE modeling only. Obtained results show that QoE function is mostly flat for small variations of input IFs which is an additional motive to propose a modification of the standard version of ASM. This study proposes several metrics that can be used to compare different dimensionality reduction approaches. We prove that the percentage of function variability described by an appropriate linear combination(s) of input IFs is always greater or equal to the percentage that corresponds to the selection of input IF(s) when the reduction degree is the same. Thus, the proposed method and metrics are useful when optimizing the number of IFs for QoE prediction and a better understanding of IFs space in terms of QoE.

Digital transformation is unstoppable and ongoing process that affects all segments of our everyday lives such as healthcare, banking and finance systems, public administration, or postal and logistics systems. One of the main engines of this process, among many, is the use of cryptocurrencies. Both of these concepts are usually addressed from the firm perspective, but they should be considered from the individual’s perspective as well. Cryptocurrencies recently face increased cyber security threats, thereby affecting the digital transformation and posing additional challenges to it. Therefore, this paper gives a brief overview of security threats and challenges characteristic for cryptocurrency technology today and discusses what can be expected tomorrow. We provide insight how these challenges can affect digital transformation process and propose ways to increase the level of protection for both individuals and organizations.

The fifth generation of mobile telecommunications (5G) is one of the most important novelties of our times due to its influence on the economy and society. The advent of 5G networks and services expands the security threats landscape and requires the implementation of adequate countermeasures. This paper presents a brief overview of 5G security threats and countermeasures from operators’ perspective. The aim is to contribute to the 5G cybersecurity knowledge collection and dissemination by summarizing the key findings and identifying next steps.

Information-Communication Technologies (ICTs) are currently used in various fields and there are many amazing inventions that are already present and make communication and life easier for us on a daily basis. The use of ICTs is less represented in the social work institutions. Therefore, this paper presents the implementation of ICTs through the chatbot application for the needs of social work created on the Tidio platform and within the webpage of the Public Institution “Center for Social Work Tešanj”. The application facilitates the work of social workers in collecting information from the users and eases the aid received by the users of social care in terms of faster responses to inquiries during emergencies, such as the COVID-19 pandemic, but also after. For the purpose of this research, an end-user survey was created and conducted with the aim of collecting user opinions on the acceptance and motivation for the use of chatbots in social work institutions. The results showed good acceptance and usage motivation of social work chatbot.

Network traffic recognition serves as a basic condition for network operators to differentiate and prioritize traffic for a number of purposes, from guaranteeing the Quality of Service (QoS), to monitoring safety, as well as monitoring and detecting anomalies. Web Real-Time Communication (WebRTC) is an open-source project that enables real-time audio, video, and text communication among browsers. Since WebRTC does not include any characteristic pattern for semantically based traffic recognition, this paper proposes models for recognizing traffic generated during WebRTC audio and video communication based on statistical characteristics and usage of machine learning in Weka tool. Five classification algorithms have been used for model development, such as Naive Bayes, J48, Random Forest, REP tree, and Bayes Net. The results show that J48 and BayesNet have the best performances in this experimental case of WebRTC traffic recognition. Future work will be focused on comparison of a wide range of machine learning algorithms using a large enough dataset to improve the significance of the results.