Diabetes mellitus has high prevalence in the ageing population and is often accompanied by other comorbidities, such as Alzheimer’s disease, and general disabilities, such as poor eyesight. These comorbidities have redefined ways in which patients use mHealth apps, including diabetes apps. The latter have proven benefits for monitoring blood glucose levels and insulin tracking in the general population. In this paper, we analyse a diabetes monitoring app DeStress Assistant (DeSA), which was developed as a part of an EU project and tested in a hospital setting. Due to the increasing number of older adults, we wanted to ensure the app was suitable for that demographic. Based on a number of supervised tests, we show that the app, which was developed with the help of workshops and feedback from tech-savvy patients and clinicians, is difficult to use by elderly users. We demonstrate that with a small number of changes it is possible to raise the usability of the app in a number of categories. We summarise the lessons learned in the discussion. Our findings demonstrate that special care needs to be taken when developing mHealth apps for the elderly population.

Cybersecurity research demands continuous monitoring of the dynamic threat landscape to detect novel attacks. Researchers and security professionals often deploy honeypot networks to intercept and examine real attack data. However, due to the volume and variety of the collected data, it is very challenging for security analysts to investigate the attacks, compare their characteristics and infer their potential connections. To this end, we propose a novel graph-based cyberattack model for storing, analyzing, and visualizing honeynet-captured attacks as the main contribution of our work. Our model enables attack graph analysis and presents the attack data analogous to the Cyber Kill Chain framework to enable intuitive visualizations. We construct the attack graph by decomposing the intercepted attacks into a set of unique entities (represented as nodes) and actions (represented as edges) and merge them into a global attack graph. We develop a user-centric, interactive attack analysis and visualization tool that leverages the proposed model to aid the heuristic cyberattack investigation. We describe the design and technical implementation of the developed model and visual-interactive tool in detail. Finally, we demonstrate the developed tools and validate the model in an analysis of real-world attack data captured on our own distributed honeypot platform. We use the attack model and (sub)graph visualizations to depict attack topologies, identify recurring attackers, and quantify detected malware types. We also leverage graph data science algorithms to uncover and rank malware distribution networks, reveal hidden links between the attackers, and cluster the attack entities to identify potential botnets.

Real-time data about various traffic events and conditions—offences, accidents, dangerous driving, or dangerous road conditions—is crucial for safe and efficient transportation. Unlike roadside infrastructure data which are often limited in scope and quantity, crowdsensing approaches promise much broader and comprehensive coverage of traffic events. However, to ensure safe and efficient traffic operation, assessing trustworthiness of crowdsourced data is of crucial importance; this also includes detection of intentional or unintentional manipulation, deception, and spamming. In this paper, we design and demonstrate a road traffic event detection and source reputation assessment system for unreliable data sources. Special care is taken to adapt the system for operation in decentralized mode, using smart contracts on a Turing-complete blockchain platform, eliminating single authority over such systems and increasing resilience to institutional data manipulation. The proposed solution was evaluated using both a synthetic traffic event dataset and a dataset gathered from real users, using a traffic event reporting mobile application in a professional driving simulator used for driver training. The results show the proposed system can accurately detect a range of manipulative and misreporting behaviors, and quickly converges to the final trust score even in a resource-constrained environment of a blockchain platform virtual machine.

Digitalization of our economy and society has ushered in notable productivity increases but has also exposed more of our infrastructures and systems to cyberattacks. This trend is exacerbated by the proliferation of poorly designed Internet of Things (IoT) devices and cloud services, which often lack appropriate security measures, either due to bugs or configuration mistakes. In this article, we propose, validate, and critically evaluate a flexible honeypot system based on the Hypertext Transfer Protocol (HTTP) that can mimic any HTTP-based service and application. This covers a large share of IoT devices, including black box devices with no software or firmware available for emulation, as well as cloud- and web-based services. We validate the system by implementing 14 services and by running a 4-month experiment, collecting data from attackers. We propose a novel data enrichment mechanism for identifying internet scanning services, as well as several other data collection and enrichment approaches. Finally, we present some results and visualizations of the data collection experiment, demonstrating possible applications and future use cases, as well as potential drawbacks of such systems.

Architecture for click-to-dial over IP television is discussed, utilizing Parlay X Web services to communicate with telephony network elements. The architecture and individual elements of the system are presented in detail, including a gateway to call servers and telephony switches. The implemented system enables IPTV users to establish calls over mobile or fixed phones by pushing a button on their IPTV remote control. Additionally, we describe a method for automatic phone number recognition from live video that communicates the data to IPTV users via Web services architecture, enabling them to initiate a call easily to any number displayed on the video.