# Cybersecurity Risk Assessment in Smart City Infrastructures +
## 4 The Neural Network Model for the Cybersecurity Risk Assessment  
It is proposed to reduce the task of assessing the cybersecurity risks to the task of classification. Let \(S=\{S_{1},\dots,\ S_{n}\}\) be the set of the considered states (modes of operation) of a large-scale dynamic network, among which there are both safe and unsafe states. Each state is characterized by a certain value of the cybersecurity risk. It is required, with a minimum time for accumulating statistical information \(\ t_{k}\to min\), to identify the risks of the cybersecurity violations of the dynamic network with sufficient accuracy. As part of the task, a methodology for identifying the risks of cybersecurity violations has been developed (Figure 12). The technique consists of four stages:  
1. Preparatory stage;
2. Formation of training samples, the base of scenarios (BS) of the dynamic networks operation modes;
3. Classification;
4. The cybersecurity risk assessment. The set of types of the network nodes \(\mathit{Types}=\{T_{1},T_{2},\dots,T_{l}\}\), selected for the specific dynamic network, forms a time series \(X=\{\{x_{1}(t_{1}),\ x_{1}(t_{2}),\dots,x_{1}(t_{k})\}\), \(\{x_{2}(t_{1}),\ x_{2}(t_{2}),\dots,x_{2}(t_{k})\},\dots,\ \{x_{m}(t_{1}),\ x_{m}(t_{2}),\dots,x_{m}(t_{k})\}\}\), which are the time-synchronized network characteristics (interaction and influence coefficients, probability of cyberattacks, etc.) from various devices \(D=\{D_{1},D_{2},\dots,D_{m}\}\) located on the controlled node of the dynamic network. Figure 11: The taxonomy of the risk assessment methods. The key indicators in assessing cybersecurity risks are the likelihood of a cyberattack occurring and the damage caused, usually expressed in monetary terms. However, when assessing risks in the smart city networks, it is important to understand that not only the integrity, confidentiality, and availability of data but also the life and health of people are at risk. Typically, the cybersecurity risk assessment is a classification problem and a regression tree that either classifies the current cybersecurity risk level as acceptable or unacceptable or predicts classes based on past data. Although traditional statistical analysis and mathematical models are widely used in various cybersecurity risk assessment analysis scenarios, the artificial neural network models are more flexible and capable of modeling more complex nonlinear functions than classical statistical models such as linear discriminant analysis and logistic regression. For example, for a neural network model using a logistic function, its more hidden layers allow the study of complex nonlinear relationships. In addition, the advantage of the ANNs is that they do not require an explicit indication of the functional relationship between the dependent and independent variables. The ANN weighing process simply assigns less weight to variables that it predicts will be less important. Based on the analysis of the problem, it has been decided to use the perceptron model and the backpropagation algorithm as a training one. This type of ANN is pretty well researched and described in scientific reports [40]. The advantage of a multilayer perceptron is the ability to solve the linearly inseparable problems, high classification accuracy with a small dimension of the input data [41]. Each ANN's element builds a weighted sum of its inputs, adjusted in the form of a term, and then passes this activation value through the transfer function, thus obtaining the output value of this element. Elements are organized in a layered topology with direct signal transmission. Such an ANN can easily be interpreted as an input-output model, in which weights and threshold values (offsets) are the free parameters of the model. A typical backpropagation neural network consists of a three-layer structure: input nodes, output nodes, and hidden nodes.