Photo-Set: A Proposed Dataset and Benchmark for Physics-Based Cybersecurity Monitoring in Photovoltaic Systems

Afroz Mokarim, Giovanni Battista Gaggero, Giulio Ferro, Michela Robba, Paola Girdinio () and Mario Marchese
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Afroz Mokarim: Department of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Via all’Opera Pia 11A, 16145 Genoa, Italy
Giovanni Battista Gaggero: Department of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Via all’Opera Pia 11A, 16145 Genoa, Italy
Giulio Ferro: Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via all’Opera Pia 13, 16145 Genoa, Italy
Michela Robba: Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via all’Opera Pia 13, 16145 Genoa, Italy
Paola Girdinio: Department of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Via all’Opera Pia 11A, 16145 Genoa, Italy
Mario Marchese: Department of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Via all’Opera Pia 11A, 16145 Genoa, Italy

Energies, 2025, vol. 18, issue 19, 1-25

Abstract: Modern photovoltaic (PV) systems face increasing cybersecurity threats due to their integration with smart grid infrastructure. While previous research has identified vulnerabilities, the lack of standardized datasets has hindered the development and evaluation of detection algorithms. Building upon our previously introduced Photo-Set dataset, this paper presents a benchmark evaluation of anomaly detection algorithms for PV cybersecurity applications. We evaluate three state-of-the-art algorithms (One-Class SVM, Isolation Forest, and Local Outlier Factor) across 12 attack scenarios, establishing performance baselines and identifying algorithm-specific strengths and limitations. Our experimental results reveal a clear detectability hierarchy. This work proposes a standardized benchmark for PV cybersecurity research and provides the industry with evidence-based guidance for security system deployment.

Keywords: dataset; cybersecurity; anomaly detection; smart grid; photovoltaic; distributed energy resources (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: 2025
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