Countering Evasion Attacks for Smart Grid Reinforcement Learning-Based Detectors

Author:

El-Toukhy Ahmed T.1ORCID,Mahmoud Mohamed M. E. A.1ORCID,Bondok Atef H.1ORCID,Fouda Mostafa M.2ORCID,Alsabaan Maazen3ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Tennessee Tech University, Cookeville, TN, USA

2. Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID, USA

3. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Funder

King Saud University, Riyadh, Saudi Arabia, through the Researchers Supporting Project

Idaho State University funding for the Center for Advanced Energy Studies (CAES), USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Evasive attacks against autoencoder-based cyberattack detection systems in power systems;Energy and AI;2024-09

2. Attacking Learning-based Models in Smart Grids: Current Challenges and New Frontiers;The 15th ACM International Conference on Future and Sustainable Energy Systems;2024-05-31

3. Securing Smart Grids: Deep Reinforcement Learning Approach for Detecting Cyber-Attacks;2024 International Conference on Smart Applications, Communications and Networking (SmartNets);2024-05-28

4. A Distillation-Based Attack Against Adversarial Training Defense for Smart Grid Federated Learning;2024 IEEE 21st Consumer Communications & Networking Conference (CCNC);2024-01-06

5. Secured Cluster-Based Electricity Theft Detectors Against Blackbox Evasion Attacks;2024 IEEE 21st Consumer Communications & Networking Conference (CCNC);2024-01-06

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