GAN_GRID

GAN_GRID

GAN-GRID: A Novel Adversarial Attack on Smart Grid Stability Prediction
Papaer Available»

Emad Efatinasab · Alessandro Brighente · Mirco Rampazzo . Nahal Azadi · Mauro Conti

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🗣️ Citation

Please, cite this work when referring to GAN_GRID.

@InProceedings{10.1007/978-3-031-70879-4_19,
author="Efatinasab, Emad
and Brighente, Alessandro
and Rampazzo, Mirco
and Azadi, Nahal
and Conti, Mauro",
editor="Garcia-Alfaro, Joaquin
and Kozik, Rafa{\l}
and Chora{\'{s}}, Micha{\l}
and Katsikas, Sokratis",
title="GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction",
booktitle="Computer Security -- ESORICS 2024",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="374--393",
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.",
isbn="978-3-031-70879-4"
}

🧩 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. Central to its operation are goals encompassing grid stability, enhanced power system performance, security, and reduced operational costs. Accurate energy demand prediction is paramount, ensuring optimal energy availability and mitigating costly production or usage errors. Leveraging state-of-the-art machine learning algorithms, including the chosen XGBoost model and a proposed LSTM-based deep learning model, we explore the publicly available smart grid dataset for the stability prediction task. However, the stability of a smart grid is intricately linked to its resilience against cyberattacks. In this study, we propose GAN-GRID a novel adversarial attack targeting the stability prediction system, 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.

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⚙️ Usage

To execute the attacks or to deploy the FaultGuard framework, start by cloning the repository:

git clone https://github.com/emadef1/GAN_GRID.git
cd GAN_GRID

^{NOTE: if you're accessing this data from the anonymized repository, the above command might not work..}

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