Real-Time Locational Detection of Stealthy False Data Injection Attack in Smart Grid: Using Multivariate-Based Multi-Label Classification Approach

Hanem I. Hegazy, Adly S. Tag Eldien, Mohsen M. Tantawy, Mostafa M. Fouda and Heba A. TagElDien
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Hanem I. Hegazy: Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
Adly S. Tag Eldien: Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
Mohsen M. Tantawy: Network Planning Department, National Telecommunication Institute (NTI), Cairo 11768, Egypt
Mostafa M. Fouda: Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
Heba A. TagElDien: Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt

Energies, 2022, vol. 15, issue 14, 1-18

Abstract: Recently, false data injection attacks (FDIAs) have been identified as a significant category of cyber-attacks targeting smart grids’ state estimation and monitoring systems. These cyber-attacks aim to mislead control system operations by compromising the readings of various smart grid meters. The real-time and precise locational identification of FDIAs is crucial for smart grid security and reliability. This paper proposes a multivariate-based multi-label locational detection (MMLD) mechanism to detect the presence and locations of FDIAs in real-time measurements with precise locational detection accuracy. The proposed architecture is a parallel structure that concatenates Long Short-Term Memory (LSTM) with Temporal Convolutional Neural Network (TCN). The proposed architecture is trained using Keras with Tensorflow libraries, and its performance is verified using an IEEE standard bus system in the MATPOWER package. Extensive testing has shown that the proposed approach effectively improves the presence-detection accuracy for locating stealthy FDIAs in small and large systems under various attack conditions. In addition, this work provides a customized loss function for handling the class imbalance problem. Simulation results reveal that our MMLD technique has a modest advantage in some aspects. First, our mechanism outperforms benchmark models because the problem is formulated as a multivariate-based multi-label classification problem. Second, it needs fewer iterations for training and reaching the optimal model. More specifically, our approach is less complex and more scalable than benchmark algorithms.

Keywords: smart grid; FDIA; LSTM; CNN; MMLD; LSTM-TCN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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