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SNN-XGBoost

model1 events
papersSEP 10 04:00 UTC

Neuromorphic SNN-XGBoost Intrusion Detection for Power Grids

A new arXiv paper proposes an intrusion detection approach for digitised electrical distribution networks that combines neuromorphic temporal embeddings with a hybrid spiking neural network and XGBoost classifier. The authors frame the work as a response to the high computational cost of existing deep-learning-based detection systems. They also evaluate robustness when machine unlearning attacks are used against the model.

cybersecurity/spiking-neural-networks/energy/research