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Simultaneous Detection for Multiple Anomaly Data in Internet of Energy Based on Random Forest by Li, Qiang (author);Zhang, Limei (author);Zhang, Guanghui (author);Ouyang, Hanyi (author);Bai, Muke (author) is a Computer Science article available to read on EtoBox.

What is Simultaneous Detection for Multiple Anomaly Data in Internet of Energy Based on Random Forest about?

Anomaly Data Detection plays a core role in defensing cyber-attacks, ensuring reliability, protecting security and improving robustness in smart grid. This study constructs a fast method to detect multi-node anomaly data including outlier and implicit characteristics. The outliers are determined according historical datasets, while implicit features invoked are summed up from satisfying with operation requirements of smart grid. For accomplishment of simultaneous detection for multiple abnormal data, Improved Random Forest Algorithm (IRFA) is proposed through modifying the following aspects: data set reconstruction, bootstrap sampling, decision tree generation and majority voting. In addition, parallel strategies are designed to improve algorithmic efficiency. Plenty of simulations are used to evaluate performance of the developed method through plenty of loads information from various transformers. Here not only explore the impact of various features and decision trees on algorithm’s performances, but also investigate the correspondence between node number and decision tree. And the accuracy in this study is compared with that of BP Neural Network and Support Vector Machine. Simul

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Author

Li, Qiang (author);Zhang, Limei (author);Zhang, Guanghui (author);Ouyang, Hanyi (author);Bai, Muke (author)

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)