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Deep Learning-Based Cyber-Attack Detection Model for Smart Grids. (arXiv:2312.08810v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
In this paper, a novel artificial intelligence-based cyber-attack detection
model for smart grids is developed to stop data integrity cyber-attacks (DIAs)
on the received load data by supervisory control and data acquisition (SCADA).
In the proposed model, first the load data is forecasted using a regression
model and after processing stage, the processed data is clustered using the
unsupervised learning method. In this work, in order to achieve the best
performance, three load forecasting methods (i.e. extra tree regression (ETR), …
acquisition artificial artificial intelligence attack attacks control cyber cyber-attack data data integrity deep learning detection integrity intelligence novel scada smart