Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids

2023/03/28 by Carmelo Ardito, Yashar Deldjoo, Ardito, Carmelo +9
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #Electricity Theft Detection Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Grid Security and Resilience

paper · pdf · doi:10.48550/arxiv.2303.18136

openalex publication_date 2023/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate the challenges associated with the security of machine learning (ML) applications in the smart grid scenario. Indeed, the robustness and security of these data-driven algorithms have not been extensively studied in relation to all power grid applications. We demonstrate first that the deep neural network method used in the smart grid is susceptible to adversarial perturbation. Then, we highlight how studies on fault localization and type classification illustrate the weaknesses of present ML algorithms in smart grids to various adversarial attacks

Related

• Is Machine Learning in Power Systems Vulnerable?
• Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review
• Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids
• Adversarial Attacks on Deep Learning-Based False Data Injection Detection in Differential Relays
• Attack Detection and Localization in Smart Grid with Image-based Deep Learning