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Related Experiment Video

Updated: Mar 16, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

FalsEye: proactive detection of false data injection attacks in smart grids using IceCube-optimised ensemble

Ahmed N Sheta¹, Samaa F Osman¹, Abdelfattah A Eladl²

¹Electrical Engineering Department, Faculty of Engineering, Mansoura University, El-Mansoura, 35516, Egypt.

Scientific Reports

|March 15, 2026

Summary

This summary is machine-generated.

This study introduces a new method to detect False Data Injection Attacks (FDIAs) in smart grids (SGs). The advanced framework significantly improves detection accuracy and cyber resilience for smart grid infrastructure.

Keywords:

Cyber-attack False data injection attack GridsearchCV Machine learning Smart grids Voting classifier

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Area of Science:

Cybersecurity
Smart Grid Technology
Artificial Intelligence

Background:

False Data Injection Attacks (FDIAs) pose a significant threat to smart grid (SG) stability and reliability.
Existing detection methods struggle with data imbalance and suboptimal model parameters.

Purpose of the Study:

To propose a proactive detection framework for FDIAs in SGs.
To enhance the accuracy and robustness of FDIA detection systems.

Main Methods:

Implemented a Voting Classifier ensemble combining ExtraTrees, CatBoost, and LightGBM.
Utilized the IceCube Optimization (IO) algorithm for hyperparameter tuning.
Incorporated Adaptive Synthetic oversampling to address class imbalance.

Main Results:

The IO Voting Classifier demonstrated superior F1-scores and a better precision-recall trade-off than conventional methods.
Achieved 99% accuracy, 92% precision, 98% recall, and 95% F1-score.
The optimized framework significantly improved FDIA detection rates.

Conclusions:

Combining metaheuristic optimization with ensemble learning offers a robust solution for cyber-resilient SGs.
The proposed framework effectively mitigates class imbalance and enhances detection performance.
This approach holds considerable potential for securing smart grid infrastructures.