Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids

Hany Habbak, Mohamed Mahmoud (), Mostafa M. Fouda (), Maazen Alsabaan, Ahmed Mattar, Gouda I. Salama and Khaled Metwally
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Hany Habbak: Department of Computer Engineering and AI, Military Technical College, Cairo 11766, Egypt
Mohamed Mahmoud: Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
Mostafa M. Fouda: Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA
Maazen Alsabaan: Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Ahmed Mattar: Department of Computer Engineering and AI, Military Technical College, Cairo 11766, Egypt
Gouda I. Salama: Department of Computer Engineering and AI, Military Technical College, Cairo 11766, Egypt
Khaled Metwally: Department of Computer Engineering and AI, Military Technical College, Cairo 11766, Egypt

Energies, 2023, vol. 16, issue 20, 1-28

Abstract: In the smart grid, malicious consumers can hack their smart meters to report false power consumption readings to steal electricity. Developing a machine-learning based detector for identifying these readings is a challenge due to the unavailability of malicious datasets. Most of the existing works in the literature assume attacks to compute malicious data. These detectors are trained to identify these attacks, but they cannot identify new attacks, which creates a vulnerability. Very few papers in the literature tried to address this problem by investigating anomaly detectors trained solely on benign data, but they suffer from these limitations: (1) low detection accuracy and high false alarm; (2) the need for knowledge on the malicious data to compute good detection thresholds; and (3) they cannot capture the temporal correlations of the readings and do not address the class overlapping issue caused by some deceptive attacks. To address these limitations, this paper presents a deep support vector data description ( DSVDD ) based unsupervised detector for false data in smart grid. Time-series readings are transformed into images, and the detector is exclusively trained on benign images. Our experimental results demonstrate the superior performance of our detectors compared to existing approaches in the literature. Specifically, our proposed DSVDD -based schemes have exhibited improvements of 0.5% to 3% in terms of recall and 3% to 9% in terms of the Area Under the Curve ( AUC ) when compared to existing state-of-the-art detectors.

Keywords: false data detection; electricity theft; smart meters; automatic metering infrastructure; smart power grid; deep-SVDD; one-class classification (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: 2023
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