Evasion Attacks in Smart Power Grids: A Deep Reinforcement Learning Approach

In smart power grids, certain customers are motivated by financial gains to manipulate electricity consumption data, aiming to reduce their bills. Despite the development of machine learning-based detectors, these systems remain vulnerable to evasion attacks. This paper investigates the susceptibility of deep reinforcement learning (DRL)-based detectors to evasion attacks. We propose an evasion attack model that employs the double deep Q learning (DDQN) algorithm for a black-box attack scenario. Our model generates adversarial evasion samples by altering malicious consumption data, tricking detectors into classifying them as benign. Leveraging the unique attributes of reinforcement learning (RL), our model determines optimal actions for manipulating malicious data. For comparative analysis, we compare our DRL-based model with an FGSM-based attack model. Our experiments consistently demonstrate the effectiveness of our DRL-based attack model, achieving an impressive attack success rate (ASR) ranging from 92.92% to 99.96%, outperforming the FGSM-based attack model.