Abstract
Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented.
Highlights
The concept of a “smart and sustainable city” is emerging with two flagship applications worldwide
Machine learning, which can rely on adaptive baseline behavior models, is extremely effective in detecting new, unknown attacks: The combination of known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape [70,71,72,73,74]
This work has a qualitative approach. It intends to make a reflective analysis based on the documentary review on some methodologies implemented to evaluate cybersecurity risk applied to supervisory control and data acquisition (SCADA) systems for electricity companies
Summary
The concept of a “smart and sustainable city” is emerging with two flagship applications worldwide. The most significant contributions to the reliability, safety, and efficiency of the electrical network have taken place in the development of intelligent optimization algorithms, such as genetic algorithms, neural networks, game theory strategies, reinforcement learning, vector support machines, among others These previous strategies have made it possible to study the interactions in formalized security structures in response to demand in the energy markets. We conduct a comprehensive overview and analysis of smart grid architecture and different security aspects in the era of big data and artificial intelligence It is a risk-based cybersecurity framework—a set of industry standards and best practices to help SG operators manage cybersecurity risks.
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