Abstract
The article is devoted to cybersecurity risk assessment of the dynamic device-to-device networks of a smart city. Analysis of the modern security threats at the IoT/IIoT, VANET, and WSN inter-device infrastructures demonstrates that the main concern is a set of network security threats targeted at the functional sustainability of smart urban infrastructure, the most common use case of smart networks. As a result of our study, systematization of the existing cybersecurity risk assessment methods has been provided. Expert-based risk assessment and active human participation cannot be provided for the huge, complex, and permanently changing digital environment of the smart city. The methods of scenario analysis and functional analysis are specific to industrial risk management and are hardly adaptable to solving cybersecurity tasks. The statistical risk evaluation methods force us to collect statistical data for the calculation of the security indicators for the self-organizing networks, and the accuracy of this method depends on the number of calculating iterations. In our work, we have proposed a new approach for cybersecurity risk management based on object typing, data mining, and quantitative risk assessment for the smart city infrastructure. The experimental study has shown us that the artificial neural network allows us to automatically, unambiguously, and reasonably assess the cyber risk for various object types in the dynamic digital infrastructures of the smart city.
Highlights
The technological aspect of a smart city is reflected by IBM, the leading promoter of the smart city concept
We have proposed a new approach for cybersecurity risk management based on object typing, data mining, and quantitative risk assessment for the smart city infrastructure
Smart cities are increasingly being exposed to various cybersecurity impacts: complex cyberattacks on critical infrastructures by interrupting the automated control systems, hacking communications between the smart IoT/IIoT devices, blocking the VANET nodes, and other connected systems using ransomware, changing the sensing data [5]
Summary
The technological aspect of a smart city is reflected by IBM, the leading promoter of the smart city concept. The high risk of an attacker obtaining financial information, business plans, and private data as a result of hacking sensitive assets is highlighted in the research [7] For such a serious problem, it becomes important to choose an effective protection strategy. The novelty lies in the fact that, for the first time, we propose to use an artificial neural network that allows us to reasonably assess cybersecurity risks by processing big security datasets. It allows for faster response time in critical situations and makes the decision-making more effective due to deeper insights and visibility of the cybersecurity risks. The paper is organized as follows: Section 2 reviews the current types of cyber threats specific to the dynamic smart city infrastructure; Section 3 provides an overview of the related works for cybersecurity risk assessment applicable to the smart city; Section 4 proposes an artificial neural network method for the assessment of cyber risks for the smart city; Section 5 discusses the outputs of the experimental study of our method; and, the last section concludes our work and sets further plans
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