# Cybersecurity Risk Assessment in Smart City Infrastructures +
## 6 Discussion and Future Perspectives  
An analysis of the security and risk management research for the dynamic networks of the smart city has shown that the current works actively propose new approaches to risk assessment, as the traditional methods are unable to operate with the specifics of the rapidly changing network assets. The machine learning methods have already shown their effectiveness in tasks that require working with big data and hidden dependencies. Our work presents a neural network approach to assessing cybersecurity risks. Using the network simulator, it was possible to recreate the dynamic network infrastructure of a smart city. Modeling scenarios were developed, and five types of network attacks were implemented. From the data obtained in the course of modeling, a dataset was prepared, including network characteristics and economic characteristics. All assets were typed, and a threshold of the acceptable level of risk was determined for each asset type. A neural network model was developed, namely a three-layer perceptron, which was trained on labeled data, and then the classification quality was assessed on unlabeled data. The test results showed an accuracy of 98-99%, which speaks of the promise of the proposed approach. The main advantages of the proposed approach are the ability to work in rapidly changing conditions, high classification accuracy when working with big data, the possibility of dynamic risk assessment, as well as the ability to work in conditions of limited awareness of the state of the entire smart city network. In practice, to ensure high accuracy, the central node calculating the cyber risks has to collect data on the controlled network in real-time, constantly update the probabilities of network attacks, the interaction coefficients, and the influence of nodes on each other. In addition, the operation of a neural network requires significant computing power: the more nodes are in the controlled network, the more computing resources are required. Nevertheless, despite the mentioned limitations, the proposed approach is more flexible than the existing approaches discussed in Section 3. The ability to constantly supplement the training set, dynamically monitor the level of cyber threats in the smart city network, and update the parameters of nodes all favorably distinguish the proposed method from existing analogs. In the future, it is planned to continue our research in these areas:  
* Add new features to datasets (for example, various network indicators: the ratio of sent and lost packets, throughput, number of hops, etc., as well as economic indicators: ROI, ROA, and ROE);
* Compare the proposed neural network approach with other existing cybersecurity risk assessment methods. Figure 14: Comparison of the neural network approach with the traditional. **Author Contributions:** Conceptualization, M.K., V.K., and P.Z. ; methodology, M.K. and V.K. ; software, V.K. ; validation, V.K. and P.Z. ; formal analysis, V.K. ; investigation, V.K. ; resources, M.K. and V.K. ; data curation, V.K. ; writing--original draft preparation, V.K. ; writing--review and editing M.K., V.K., and P.Z. ; visualization, V.K. ; supervision, M.K., V.K., and P.Z. ; project administration, M.K., V.K., and P.Z. ; funding acquisition, M.K., V.K., and P.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** The reported study was funded by RFBR according to research project No.19-37-90001 (Sections 1, 3, 5 and 6). The reported study was funded as part of the State Task for Basic Research (code of theme: 0784-2020-0026); suppl. agreement to the agreement for the financial support No.075-03-2020-158/2, 17.03.2020 (internal No.075-GZ/SCH4575/784/2) (Sections 2 and 4). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable.