# A Survey on Cyber Risk Management for the Internet of Things
## 6 Recommendations
### Recommendations for IoT Cyber Risk Calculation  
Being the second component to IoT cyber risk assessment, the identified risk parameters are used to calculate risk in a meaningful way to determine risk. We explored multiple ways that risk can be assessed; however, risk parameters and results must be meaningful and easy to understand, aiding in risk decision making. Risk calculations can represent risk in a semi-quantitative, quantitative, or qualitative way, with researchers potentially using well-established risk calculation methods by adapting them to better suit IoT domains or using a brand-new system, which facilities cyber security standards. The common methods within the surveyed IoT cyber risk management frameworks are the uses of graph modelling, risk matrices, threat knowledge bases, and risk weighting, methods that researchers could use to determine risk within an IoT domain. Below, we suggest questions for future works and recommend ways to overcome problems within IoT cyber risk calculation processes. _How can future works use graph modelling to effectively calculate IoT risk?_ When it comes to calculating risk, graph modelling allows researchers to easily visualise risks within a network by displaying entry points, attack paths, data flow, threat propagation, and dependencies. Within our survey, there are a number of examples of graph modelling within varying IoT domains, and it is notable that different graphical models can be used in several different ways as discussed within our literature review. For example, [PERSON] et al. [PERSON] et al. (2018) use dynamic Bayesian networks to to visualise smart city nodes and links, while [PERSON] et al. [PERSON] et al. (2018) visualise dynamic changes within a smart home network. Researchers may consider using graph modelling and assess the benefits and drawbacks to using different types of graph models, dependent on available resources and functionality of IoT risk management tools. As an example, Bayesian networks are a common way to predict likelihood, showing probabilistic relationships between nodes based on conditional dependencies. A major advantage of Bayesian networks is seen when information is not fully known, which, as we described, is an issue that affects likelihood estimation. For IoT, it is integral to model the dynamic nature of IoT devices; however, like other graph-modelling methods, the more nodes and updates required, the greater the computational effort. While graph modelling is commonly used to capture risk on various parts of a network, human interactions are not represented by the works we surveyed. To combat this, researchers may need to integrate human-to-IoT interactions to assess the dependencies that users have within an IoT domain. _How can future works use risk matrices to effectively calculate IoT risk?_ Alternative to graph modelling, future works can examine the use of risk matrices to effectively calculate IoT risk, as a risk matrix provides a simpler way to visualise risk in a semi-quantitative chart format. Risk matrices need to have well-defined risk categories that are accurately comparable to one another, making different levels of risk clear. An issue surrounding risk matrices is the potential for unreliable views of impact/likelihood values and the oversimplification of risk values. In the context of healthcare devices, [PERSON] et al. [74] utilise a simple risk matrix that measures impact severity and probability, which aids in the prioritiisation of critical risks. Alternatively, for smart grids, [PERSON] et al. [47] use a matrix that reflects the relationships between threats and assets. While simple IoT risk matrices are useful, researchers could consider a more complex route. For example, an adjacency matrix can be used to represent a graph, as seen by [PERSON] et al. [53; 54]. Notably, an increasingly complex risk matrix can be used to model a dynamic system nature, which is prominent within IoT, and could also be used to emulate user interaction.