Cybersecurity Risk Assessment in Smart City Infrastructures

• Main Authors: Maxim Kalinin, Vasiliy Krundyshev, Peter Zegzhda
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Machines
• Subjects: cybersecurity; dynamic network; machine learning; network attack; neural network; risk assessment
• Online Access: https://www.mdpi.com/2075-1702/9/4/78

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.