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Electrical Engineering
Published in:

01-07-2024 | Original Paper

Real-time implementation of IoT-enabled cyberattack detection system in advanced metering infrastructure using machine learning technique

Authors: K. Naveeda, S. M. H. Sithi Shameem Fathima

Published in: Electrical Engineering | Issue 1/2025

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Abstract

The article explores the real-time implementation of an IoT-enabled cyberattack detection system in advanced metering infrastructure (AMI) using machine learning techniques. It discusses the integration of smart meters with cloud-based cybersecurity systems, focusing on anomaly detection and false data injection attacks. The system is implemented in a commercial building and utilizes algorithms like Isolation Forest and XGBoost for real-time monitoring and threat detection. The article also covers the hardware setup, communication protocols, and security measures to safeguard the system against potential cyberthreats. Additionally, it provides insights into the challenges and vulnerabilities associated with smart meters and proposes a comprehensive solution to enhance the resilience of the energy infrastructure against cyberattacks.
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Metadata
Title
Real-time implementation of IoT-enabled cyberattack detection system in advanced metering infrastructure using machine learning technique
Authors
K. Naveeda
S. M. H. Sithi Shameem Fathima
Publication date
01-07-2024
Publisher
Springer Berlin Heidelberg
Published in
Electrical Engineering / Issue 1/2025
Print ISSN: 0948-7921
Electronic ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-024-02552-z

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