Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911767092789248 |
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| author | Ardito, Carmelo Deldjoo, Yashar Di Noia, Tommaso Di Sciascio, Eugenio Nazary, Fatemeh Servedio, Giovanni |
| author_facet | Ardito, Carmelo Deldjoo, Yashar Di Noia, Tommaso Di Sciascio, Eugenio Nazary, Fatemeh Servedio, Giovanni |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_18136 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids Ardito, Carmelo Deldjoo, Yashar Di Noia, Tommaso Di Sciascio, Eugenio Nazary, Fatemeh Servedio, Giovanni Cryptography and Security Artificial Intelligence Machine Learning 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 |
| title | Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2303.18136 |