In this paper, an open-source system for a controller-free, highly interactive exploration of medical images is presented. By using a Microsoft Xbox Kinect™ as the only input device, the system's user interface allows users to interact at a distance through hand and arm gestures. The paper also details the interaction techniques we have designed specifically for the deviceless exploration of medical imaging data. Since the user interface is touch-free and does not require complex calibration steps, it is suitable for use in operating rooms, where non-sterilizable devices cannot be used.

Digitization in healthcare systems, with the wid adoption of Electronic Health Records, connected medical devices, software and systems providing efficient healthcare service delivery and management. On the other hand, the use of these systems has significantly increased cyber threats in the healthcare sector. Vulnerabilities in the existing and legacy systems are one of the key causes for the threats and related risks. Understanding and addressing the threats from the connected medical devices and other parts of the ICT health infrastructure are of paramount importance for ensuring security within the overall healthcare ecosystem. Threat and vulnerability analysis provides an effective way to lower the impact of risks relating to the existing vulnerabilities. However, this is a challenging task due to the availability of massive data which makes it difficult to identify potential patterns of security issues. This paper contributes towards an effective threats and vulnerabilities analysis by adopting Machine Learning models, such as the BERT neural language model and XGBoost, to extract updated information from the Natural Language documents largely available on the web, evaluating at the same time the level of the identified threats and vulnerabilities that can impact on the healthcare system, providing the required information for the most appropriate management of the risk. Experiments were performed based on CS news extracted from the Hacker News website and on Common Vulnerabilities and Exposures (CVE) vulnerability reports. The results demonstrate the effectiveness of the proposed approach, which provides a realistic manner to assess the threats and vulnerabilities from Natural Language texts, allowing adopting it in real-world Healthcare ecosystems.

The healthcare sectors have constantly faced significant challenge due to the rapid rise of cyber threats. These threats can pose any potential risk within the system context and disrupt the critical healthcare service delivery. It is therefore necessary for the healthcare organisations to understand and tackle the threats to ensure overall security and resilience. However, threats are continuously evolved and there is large amount of unstructured security-related textual information is available. This makes the threat assessment and management task very challenging. There are a number of existing works that consider Machine Learning models for detection and prediction of cyber attack but they lack of focus on the Natural Language Processing (NLP) to extract the threat information from unstructured security-related text. To this end, this work proposes a novel method to assess and manage threats by adopting natural language processing. The proposed method has been tailored for the healthcare ecosystem and allows to identify and assess the possible threats within healthcare information infrastructure so that appropriate control and mitigation actions can be taken into consideration to tackle the threat. In detail, NLP techniques are used to extract the useful threat information related to specific assets of the healthcare ecosystems from the largely available security-related information on Internet (e.g. cyber security news), to evaluate the level of the identified threats and to select the required mitigation actions. We have performed experiments on real healthcare ecosystems in Fraunhofer Institute for Biomedical Engineering, considering in particular three different healthcare scenarios, namely implantable medical devices, wearables, and biobank, with the purpose of demonstrating the feasibility of our approach, which is able to provide a realistic manner to identify and assess the threats, evaluate the threat level and suggest the required mitigation actions.

Publish/subscribe services have encountered considerable success in the building of modern large-scale mission-critical systems. Such systems are characterized by several non-functional requirements, among which security plays a pivotal role due to the emergence of numerous cyber attacks targeting most mission-critical systems. This requires that the adopted publish/subscribe services have to be equipped with the proper means to protect the exchanged data, to preserve their correct behavior and to face possible attack scenarios. Although significant efforts have been made in this field, many issues are still open. This paper includes an introduction to the principles of securing event notification, and an analysis of the relevant state-of-the-art by both surveying the academic literature over the period 1998-2014 on secure publish/subscribe services and overviewing the current standards for the marketed products. Next, it presents the main challenges that are still unresolved and are worthy of further attention in future research efforts.

The sophistication of cyberattacks has significantly increased, making it almost certain that organizations can be victims of cyberattacks at any time. Managing cybersecurity risk is critical for any organization so that informed decisions can be made to tackle risks before they materialize. Cybersecurity risk management is context-specific and heavily relies on the specific organization’s context. However, performing effective risk management is always challenging due to the constant changes in organizational infrastructure and security posture, including the adoption of new applications and the reconfiguration or updating of existing assets and their dependencies, as well as the potential exploitation of vulnerabilities. Despite the wider adoption of AI enabled cybersecurity risk management, there is a lack of focus on the integration of these systems along with the dynamic elements of the risk management. In this context, this research proposes a novel dynamic cyber security risk management (d-CSRM) framework to tackle this challenge by integrating dynamic parameters such as vulnerability exploitation and assets dependencies for assessing and managing the risk. The framework consists of a systemic process and makes use of a hybrid AI-enabled model that combines both linear regression and deep learning, to prioritize the vulnerabilities. Additionally, d-CSRM integrates the explainability and interpretability characteristics of the AI model for explaining model decision making and the inner working parameters. This allows the extraction of the key features that are linked with the risk and informed decision making to tackle the risks. An experiment was performed to prioritize the vulnerabilities from the widely used CVEjoin dataset using the proposed hybrid model to quantify the dynamic risk with explainability. The results show that the hybrid model effectively identifies and prioritizes the most critical vulnerabilities using the selected key features such as exploit type, exploit platform and impact that can further enhance the dynamic risk assessment.

This paper describes a novel architecture that aims to create a template for the implementation of an IT platform, supporting the deployment and integration of the different digital twin subsystems that compose a complex urban intelligence system. In more detail, the proposed Smart City IT architecture has the following main purposes: (i) facilitating the deployment of the subsystems in a cloud environment; (ii) effectively storing, integrating, managing, and sharing the huge amount of heterogeneous data acquired and produced by each subsystem, using a data lake; (iii) supporting data exchange and sharing; (iv) managing and executing workflows, to automatically coordinate and run processes; and (v) to provide and visualize the required information. A prototype of the proposed IT solution was implemented leveraging open-source frameworks and technologies, to test its functionalities and performance. The results of the tests performed in real-world settings confirmed that the proposed architecture could efficiently and easily support the deployment and integration of heterogeneous subsystems, allowing them to share and integrate their data and to select, extract, and visualize the information required by a user, as well as promoting the integration with other external systems, and defining and executing workflows to orchestrate the various subsystems involved in complex analyses and processes.