Efficient Adversarial Detection Frameworks for Vehicle-to-Microgrid Services in Edge Computing

Abstract

As Artificial Intelligence (AI) becomes increasingly integrated into microgrid control systems, the risk of malicious actors exploiting vulnerabilities in Machine Learning (ML) algorithms to disrupt power generation and distribution grows. Detection models to identify adversarial attacks need to meet the constraints of edge environments, where computational power and memory are often limited. To address this issue, we propose a novel strategy that optimizes detection models for Vehicle-to-Microgrid (V2M) edge environments without compromising performance against inference and evasion attacks. Our approach integrates model design and compression into a unified process and results in a highly compact detection model that maintains high accuracy. We evaluated our method against four benchmark evasion attacks-Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Carlini & Wagner method (C&W) and Conditional Generative Adversarial Network (CGAN) method-and two knowledge-based attacks, white-box and gray-box. Our optimized model reduces memory usage from 20MB to 1.3MB, inference time from 3.2 seconds to 0.9 seconds, and GPU utilization from 5% to 2.68%.