GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction

TL;DR

This paper introduces GAN-GRID, a new adversarial attack method that can deceive smart grid stability prediction systems with high success, highlighting the need for improved security measures.

Contribution

The paper presents GAN-GRID, a novel adversarial attack tailored for smart grid stability prediction models, demonstrating its effectiveness even without access to data or model details.

Findings

Adversaries can achieve a 99% success rate without data or model knowledge.

Manipulating sensor data can significantly disrupt grid stability.

Highlights the urgent need for security enhancements in smart grid AI systems.

Abstract

The smart grid represents a pivotal innovation in modernizing the electricity sector, offering an intelligent, digitalized energy network capable of optimizing energy delivery from source to consumer. It hence represents the backbone of the energy sector of a nation. Due to its central role, the availability of the smart grid is paramount and is hence necessary to have in-depth control of its operations and safety. To this aim, researchers developed multiple solutions to assess the smart grid's stability and guarantee that it operates in a safe state. Artificial intelligence and Machine learning algorithms have proven to be effective measures to accurately predict the smart grid's stability. Despite the presence of known adversarial attacks and potential solutions, currently, there exists no standardized measure to protect smart grids against this threat, leaving them open to new adversarial attacks. In this paper, we propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99. Also by manipulating authentic data and sensor values, the attacker can amplify grid issues, potentially undetected due to a compromised stability prediction system. These results underscore the imperative of fortifying smart grid security mechanisms against adversarial manipulation to uphold system stability and reliability.