Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting. (arXiv:2301.01832v1 [cs.LG])

The forecast of electrical loads is essential for the planning and operation
of the power system. Recently, advances in deep learning have enabled more
accurate forecasts. However, deep neural networks are prone to adversarial
attacks. Although most of the literature focuses on integrity-based attacks,
this paper proposes availability-based adversarial attacks, which can be more
easily implemented by attackers. For each forecast instance, the availability
attack position is optimally solved by mixed-integer reformulation of the
artificial neural network. To tackle this …

adversarial adversarial attacks attack attackers attacks availability countermeasures deep learning forecast forecasting instance integrity literature networks neural networks planning power system