Rec-AD applies tensor-train embedding compression plus index reordering and pipeline training to DLRM-style FDIA detection, reporting 5-74x smaller embedding tables and up to 3x faster training at nearly unchanged accuracy.
Graph neural network-based approach for detecting false data injection attacks on voltage stability,
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Rec-AD: An Efficient Computation Framework for FDIA Detection Based on Tensor Train Decomposition and Deep Learning Recommendation Model
Rec-AD applies tensor-train embedding compression plus index reordering and pipeline training to DLRM-style FDIA detection, reporting 5-74x smaller embedding tables and up to 3x faster training at nearly unchanged accuracy.