Security Enhancement of Cyber-Physical DC Ship Power System Using Scalable Deep Learning Method-Reference-Cited by-同舟云学术

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Security Enhancement of Cyber-Physical DC Ship Power System Using Scalable Deep Learning Method

Published:2023-08-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Electric Ship Technologies Symposium (ESTS)
language:
Short-container-title:

Author:

Muriithi Grace¹,Papari Behnaz²,Moghassemi Ali³,Arsalan Ali¹,Ozkan Gokhan³,Edrington Christopher Shannon³

Affiliation:

1. Clemson University,Automotive Engineering Department,South Carolina,USA

2. Automotive Engineering and Electrical and Computer Engineering Department,South Carolina,USA

3. Clemson University,Electrical and Computer Engineering Department,South Carolina,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10220341/10220344/10220475.pdf?arnumber=10220475

Reference19 articles.

1. FedDetect: A Novel Privacy-Preserving Federated Learning Framework for Energy Theft Detection in Smart Grid

2. A Federated Learning Framework for Detecting False Data Injection Attacks in Solar Farms

3. Past, Present, and Future Challenges of the Marine Vessel’s Electrical Power System

4. Federated-Learning-Based Anomaly Detection for IoT Security Attacks

5. Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

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