# Cybersecurity Risk Assessment in Smart City Infrastructures +
## 3 The Security Risk Assessment Methods
Most often, the decision tree is downward. The basis of the simplest structure of the decision tree is the answers to the questions, yes and no. For each arc of the tree structure, the numerical characteristics can be determined,for example, the amount of profit for a particular system and the probability of its receipt. In this case, it helps to take into account all possible options for action and to correlate financial results with them. Then they go on to compare alternatives. The disadvantage of this method is the limited number of solutions for the problem. In the process of building the decision tree, you have to pay attention to the tree size. It should not be too big to limit your ability to generalize the analysis and provide the correct answers. In recent years, unconventional methods for risk assessment based on artificial intelligence systems and models have become increasingly popular [32]. A feature of the mathematical apparatus of fuzzy logic is that it uses fuzzy sets with incomplete, missing, or probabilistic data [33]. For example, the authors of [34] have proposed fuzzy logic to reduce the subjectivity of the qualitative method of the risk analysis. Fuzzy logic works not so much with concepts that have clear semantic-quantitative boundaries but with a lot of probabilistic data within the boundaries. The fuzzy logic does not set up the logical relationships with specific values but with data areas with the possible updating of any value within the boundaries of this area. Correlations between certain risks can be evaluated by the machine learning models, identifying connections that are not easily observable [35]. The work [36] also discusses the important role and significance of big data and machine learning as the emerging data analysis methods for insurance risk estimation and introduces the random forest algorithm for the risk assessment. According to [37], artificial neural networks (ANN) are to be the most applied machine learning method to aid in engineering risk assessment. The use of neural network technology is appropriate in cases where the formalization of the decision process is difficult or even impossible [38]. The ANN is a very powerful modeling tool because it is a nonlinear calculating apparatus by nature. Linear modeling has been so far a fundamental one in most areas of the assessment since there are a large number of optimization methods for it. However, in the problems of the risk analysis, the linear modeling methods in the vast majority of use cases are not applicable. In [39], it is proposed to use artificial intelligence to analyze cyber risks during the colonization of Mars. The authors apply the adapted version of the aggregate loss method to compound a poisson discrete probability distribution. The following metrics are used: expected present value of the loss, shortfall probability, shortfall of the expected present value of the loss, value at risk (VaR), and conditional tail expectation (CTE). The mathematical formulas present a better understanding of the cost and risk evaluation with multiple risk calculation metrics for different cyber risk levels and tail risk under different assumptions. However, the metrics used in this work require serious mathematical methods, which lead to a slowdown in the risk analysis system. In the rapidly changing and moving environment of a smart city, finding a balance is an important task; mathematical calculations should not require a huge amount of input data and take too long. Figure 11 denotes the taxonomy of the existing risk assessment approaches. After analyzing the methods considered, it was concluded that risk assessment methods (Delphi method, brainstorming, SWIFT, etc.) based on an expert's assessment and requiring the active participation of a human cannot be applied for the dynamic infrastructures of the smart city. Methods that use scenario analysis (root cause analysis, fault tree analysis, event tree analysis, etc.)