Attacking Learning-based Models in Smart Grids: Current Challenges and New Frontiers-Reference-Cited by-同舟云学术

Attacking Learning-based Models in Smart Grids: Current Challenges and New Frontiers

Published:2024-05-31 Issue: Volume:202 Page:589-595
ISSN:
Container-title:The 15th ACM International Conference on Future and Sustainable Energy Systems
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Author:

Sánchez Gustavo¹^ORCID,Elbez Ghada¹^ORCID,Hagenmeyer Veit¹^ORCID

Affiliation:

1. KASTEL Security Research Labs, Karlsruhe Institute of Technology, Germany

Funder

Engineering Secure Systems of the Helmholtz Association (HGF) and by KASTEL Security Research Labs

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3632775.3661984

Reference54 articles.

1. Afia Afrin and Omid Ardakanian. 2023. Adversarial Attacks on Machine Learning-Based State Estimation in Power Distribution Systems. In eEnergy ’23.

2. Utkarsha Agwan, Lucas Spangher, William Arnold, Tarang Srivastava, Kameshwar Poolla, and Costas J Spanos. 2021. Pricing in prosumer aggregations using reinforcement learning. In eEnergy ’21.

3. Carmelo Ardito, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fatemeh Nazary, and Giovanni Servedio. 2023. Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids. arXiv (2023).

4. Daniel Arp Erwin Quiring Feargus Pendlebury Alexander Warnecke Fabio Pierazzi Christian Wressnegger Lorenzo Cavallaro and Konrad Rieck. 2022. Dos and don’ts of machine learning in computer security. In USENIX Security ’22.

5. Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion (2020).