Comparative Analysis between Supervised and Anomaly Detectors Against Electricity Theft Zero-Day Attacks-Reference-Cited by-同舟云学术

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Comparative Analysis between Supervised and Anomaly Detectors Against Electricity Theft Zero-Day Attacks

Published:2024-07-22 Issue: Volume: Page:706-711
ISSN:
Container-title:2024 International Telecommunications Conference (ITC-Egypt)
language:
Short-container-title:

Author:

Badr Mahmoud M.¹,Baza Mohamed²,Rasheed Amar³,Kholidy Hisham¹,Abdelfattah Sherif⁴,Zaman Tarannum Shaila⁵

Affiliation:

1. SUNY Polytechnic Institute,Department of Network and Computer Security,Utica,NY,USA

2. College of Charleston,Department of Computer Science,Charleston,SC,USA

3. Sam Houston State University,Department of Computer Science,Huntsville,TX,USA

4. Bradley University,Department of Computer Science and Information Systems,IL,USA

5. SUNY Polytechnic Institute,Department of Computer and Information Science,Utica,NY,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10620403/10620452/10620537.pdf?arnumber=10620537

Reference20 articles.

1. Recent Advances Towards Securing Smart Grids with Blockchain

2. Privacy-Preserving Federated-Learning-Based Net-Energy Forecasting

3. Privacy-Preserving and Communication-Efficient Energy Prediction Scheme Based on Federated Learning for Smart Grids

4. Secured Cluster-Based Electricity Theft Detectors Against Blackbox Evasion Attacks

5. Security and privacy preservation for smart grid AMI using machine learning and cryptography;Badr,2022

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