A security indicator is a sign that shows us what something is like or how a situation is changing and can aid us in making informed estimations on cyber risks. There are many different breeds of security indicators, but, unfortunately, they are not always easy to apply due to a lack of available or credible sources of data. This paper undertakes a systematic mapping study on the academic literature related to cyber security indicator data. We identified 117 primary studies from the past five years as relevant to answer our research questions. They were classified according to a set of categories related to research type, domain, data openness, usage, source, type and content. Our results show a linear growth of publications per year, where most indicators are based on free or internal technical data that are domain independent. While these indicators can give valuable information about the contemporary cyber risk, the increasing usage of unconventional data sources and threat intelligence feeds of more strategic and tactical nature represent a more forward-looking trend. In addition, there is a need to take methods and techniques developed by the research community from the conceptual plane and make them practical enough for real-world application.

The proliferation of IoT devices on the Internet is currently in the billions, and projections anticipate that there will be 50 billion connected IoT devices in the IoT market by 2030. This rapid expansion will result in a substantial increase in data, which includes personal data, generated by these IoT devices. It is estimated that the data volume will reach 73.1 zettabytes by 2025. To fully realize the substantial benefits of the IoT, it is imperative to facilitate the responsible utilization and sharing of this data among various stakeholders. However, it is crucial to establish robust security and trust mechanisms to ensure data integrity and privacy during data sharing. Our objective is to summarize and assess the research efforts that address secure IoT data sharing. We systematically review the state-of-the-art techniques ensuring and preserving security in the IoT data-sharing environment through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our SLR results can help readers to obtain (i) an overview of existing secure IoT data-sharing approaches and related issues, (ii) a deep-dive into Edge-focused secure IoT data sharing solutions, and (iii) research directions that require attention from the research community for follow-up work.

Background: During major maintenance projects on offshore installations, flotels are often used to accommodate the personnel. A gangway connects the flotel to the installation. If the offshore conditions are unfavorable, the responsible operatives need to decide whether to lift (disconnect) the gangway from the installation. If this is not done, there is a risk that an uncontrolled autolift (disconnection) occurs, causing harm to personnel and equipment. Objectives: We present a decision support model, developed using the DEXi tool for multi-criteria decision making, which produces advice on whether to disconnect/connect the gangway from/to the installation. Moreover, we report on our development method and experiences from the process, including the efforts invested. An evaluation of the resulting model is also offered, primarily based on feedback from a small group of offshore operatives and domain experts representing the end user target group. Methods/Approach: The decision support model was developed systematically in four steps: establish context, develop the model, tune the model, and collect feedback on the model. Results: The results indicate that the decision support model provides advice that corresponds with expert expectations, captures all aspects that are important for the assessment, is comprehensible to domain experts, and that the expected benefit justifies the effort for developing the model. Conclusions: We find the results promising, and believe that the approach can be fruitful in a wider range of risk-based decision support scenarios. Moreover, this paper can help other decision support developers decide whether a similar approach can suit them