Intrusion Detection in Carbon Neutral Data Centers Based on Integrated Renewable Energy Generation

Min ZHANG

Abstract


Energy systems play a significant role in the current world, involving not only fossil fuels but also renewable energy sources such as solar and wind, as well as battery storage. The production of renewable energy is crucial for reducing carbon emissions worldwide and supporting the development of carbon-neutral infrastructure, such as data centers, that involve energy production alongside cybersecurity concerns. Therefore, predicting the intrusion status of interconnected data centers powered by renewable energy is crucial to ensure their continued performance. Machine learning (ML) is effective in developing precise models of nonlinear relationships among variables in energy production and cybersecurity. To ensure methodological robustness, multicollinearity among predictor variables is assessed using the Variance Inflation Factor method, with a threshold of 5. In addition, to ensure representational fairness between classes in imbalanced datasets, SMOTE-ENC is used for oversampling. Moreover, to ensure the model's reliability, K-fold cross-validation and the Brier score are implemented. Three base classifiers are used, optimized using the Heavy Rain Optimization Algorithm (HROA) and the Kangaroo Optimization Algorithm (KOA). Additionally, to compare model performance, a Wilcoxon signed-rank test is used, and Copula analysis is used to assess interdependencies among variables. In summary, the results show that the HGBC (H1) performs better in terms of accuracy and reliability, with the lowest Brier score.


Keywords


Renewable Energy; Intrusion Status; Energy Sources; Copula; Brier Score

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v16i3.17118.g9255

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