Detection of False Data Injection Intrusions in Smart Power Grids: A Machine Learning Fusion Approach
mohammadreza pourshirazi
1
(
Islamic Azad university Marvdasht Faculty of Engineering
)
Mohsen Simab
2
(
Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
)
سیدعلیرضا میرزایی
3
(
Department of Electrical Engineering, Dariun Branch, Islamic Azad University, Shiraz, Iran
)
Bahador Fani
4
(
Department of Electrical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
)
Keywords: Smart grids, Power systems, Intelligent algorithms, False data injection, Data fusion, Intrusion detection, Machine learning.,
Abstract :
In this paper, a novel approach is proposed for False Data Injection (FDI) intrusion detection in smart power grids, which utilizes the fusion of four machine learning base methods including Random Forest (RF), Gradient Boosting Machine (GBM), Linear Discriminant Analysis (LDA), and Logistic Regression (LR). The outputs of these methods are integrated using an intelligent algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) to achieve higher accuracy and efficiency in detecting these types of intrusions. After extracting and reducing the number of discriminative features extracted from Phasor Measurement Units (PMUs), using the Random Forest dimensionality reduction technique, the optimal state vectors are selected and labeled for use in the training and testing phase of the base classifiers. Then, after individual training of the base algorithms and hyperparameter optimization, the base models are applied to the test vector, and the predicted output of each model is determined to detect anomalous conditions. By intelligently integrating the outputs of the base models using an ANFIS-based algorithm, it is possible to distinguish normal samples from anomalies with high accuracy and low sensitivity to noise and uncertainty in the measurement data. The proposed method was evaluated on the IEEE 14-bus power system. The simulation results show that the proposed fusion approach outperforms the existing baseline methods in FDI detection. This approach significantly reduces the False Positive Rate (FPR) and increases the Detection Rate (DR), which demonstrates its application as an effective tool for enhancing the security of smart grids against FDI intrusions.
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