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The study investigates how resilience and efficiency can be improved in the energy sector using cybersecurity. The main objective of the paper is to enable organizations in the energy sector to develop a robust framework to manage renewable energy storage effectively with little to no cost consequence because of cyberattacks. A survey data saved in the repository of Kaggle was extracted and utilized for the purpose of the study, it contains cybersecurity indicators such as anomaly score, log source, attack types, attacks severity, and response action, which were the features adopted for developing a model, while the target variable was renewable energy generation. A machine learning approach was adopted, with the Rsquared indicating that 61 % variation in renewable energy store is explained by the features included in the model. The study recommends that there should be an increased investment in cybersecurity structure to safeguard energy systems against cyber threats. Also, stakeholders should implement comprehensive training programs to enhance cybersecurity awareness and skills of personnel working in the energy sector.
Ogunleye et al. (Fri,) studied this question.
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