Repetitive Backdoor Attacks and Countermeasures for Smart Grid Reinforcement Incremental Learning

In smart grids, smart meters (SMs) transmit power consumption data to utilities for billing and energy management. However, compromised SMs can report low consumption to reduce electricity bills. Deep reinforcement learning (DRL) detectors have recently been proposed to detect these attacks due to their adaptability to new attacks and changes in power consumption patterns. This paper explores backdoor attacks targeting DRL detectors during training, aiming to introduce a vulnerability in the detector. These attacks make the detector misclassify false low-consumption data when trigger samples are used while maintaining normal classification accuracy otherwise. We propose a DRL-based attack model that generates stealthy and unique trigger samples using cosine similarity. Our evaluations show the attack is initially highly successful, but its success diminishes with honest data used for incremental training of the detector. To sustain high success rates, attackers must influence incremental training. We also propose defenses, including data filtration during the preparation stage, adversarial training for the defense model during the training stage, and a combined approach, with experiments validating their effectiveness.