LSTM-based Load Forecasting Robustness Against Noise Injection Attack in Microgrid
This addresses security vulnerabilities in microgrid load forecasting for energy management, but it is incremental as it applies existing methods to a specific attack scenario.
The paper investigates the robustness of an LSTM neural network for electric load forecasting in a microgrid against black-box Gaussian noise injection attacks, finding that noise increases the mean absolute error from 0.047 MW to 0.097 MW at SNR=6 dB, and proposes using a low-pass filter to mitigate the attack.
In this paper, we investigate the robustness of an LSTM neural network against noise injection attacks for electric load forecasting in an ideal microgrid. The performance of the LSTM model is investigated under a black-box Gaussian noise attack with different SNRs. It is assumed that attackers have just access to the input data of the LSTM model. The results show that the noise attack affects the performance of the LSTM model. The load prediction means absolute error (MAE) is 0.047 MW for a healthy prediction, while this value increases up to 0.097 MW for a Gaussian noise insertion with SNR= 6 dB. To robustify the LSTM model against noise attack, a low-pass filter with optimal cut-off frequency is applied at the model's input to remove the noise attack. The filter performs better in case of noise with lower SNR and is less promising for small noises.