Adversarial Machine Learning Against False Data Injection Attack Detection for Smart Grid Demand Response

Abstract

Distributed demand response (DR) is used in smart grids to allow utilities to balance the power supply with the demand by modulating the consumer's behavior by varying the price according to consumption patterns and forecasts. False data injection (FDI) attacks of DR can cause large economical losses for utilities, equipment damage, and issues with power flows. Recently, FDI attack detection methods based on deep learning models have been proposed and these methods have better detection performance as compared to traditional approaches. However, deep learning based models may be vulnerable to adversarial machine learning (AML) attacks. In this paper, we demonstrate the vulnerability of state-of-the-art deep learning based FDI attack detectors in DR scenarios to AML attacks. We propose a new black-box FDI attack framework to fabricate power demands in distributed DR scenarios that is capable of deceiving deep learning based FDI attack detection. The evaluation results show that the proposed AML framework can significantly decrease the FDI detection models accuracy and outperforms other AML techniques proposed in literature.