Transferable Adversarial Attack Against Deep Reinforcement Learning-Based Smart Grid Dynamic Pricing System

Abstract

Severe damage caused by transferable adversarial attacks has emerged as a prominent concern in recent years, especially in the smart grid. The security issue of the deep reinforcement learning (DRL)-based dynamic pricing system is directly related to the grid's reliability. Previous works have primarily focused on the attacks' transferability from the perspective of model architecture, whereas the concept of distribution bias offers a novel and relatively underexplored viewpoint. In this work, we propose transferable adversarial attacks with distribution (TAD) targeting the DRL model. The adversary emphasizes destroying the target model with the masqueraded malicious dataset while ensuring stealthiness. Concretely, the masqueraded dataset generated by the attacker is required to have a similar distribution to the original dataset, while perturb some of the critical samples to help the target model misdirect to the nonoptimal policy. To this end, we propose an innovative model named Masquerader, which leverages a variational auto-encoder and incorporates three elaborate loss functions to constrain the distribution and deviation of malicious samples. Extensive experiments in a DRL-based dynamic pricing system indicate that our attack strategy TAD could successfully perturb the target model's output. The aberrant flatness of retail prices and the grid system's reduction in daily profits further validate the attack's transferability and harmfulness.