A Hybrid Machine Learning-Based Framework for Data Injection Attack Detection in Smart Grids Using PCA and Stacked Autoencoders

A Hybrid Machine Learning-Based Framework for Data Injection Attack Detection in Smart Grids Using PCA and Stacked Autoencoders

Cyberattacks, especially data injection attacks, are becoming more common as smart grids are increasingly interconnected. In addition, accurate and unbiased high-quality data is required for model training. Most of the data we collect from the real world is sparse, incomplete, inconsistent, and skew...

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Bibliographic Details
Main Authors: Shahid Tufail, Hasan Iqbal, Mohd Tariq, Arif I. Sarwat
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Photovoltaic (PV) systems
grid-connected PV systems
machine learning algorithms
random forest
autoencoders
multi-layer perceptron (MLP)
Online Access:https://ieeexplore.ieee.org/document/10892133/
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https://ieeexplore.ieee.org/document/10892133/

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