Federated Deep Learning Model for False Data Injection Attack Detection in Cyber Physical Power Systems Article Swipe
Firdous Kausar
,
Simone Del Deo
,
Sajid Hussain
,
Ziyaul Haque
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17215337
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17215337
Cyber-physical power systems (CPPS) integrate information and communication technology into conventional electric power systems to facilitate bidirectional communication of information and electric power between users and power grids. Despite its benefits, the open communication environment of CPPS is vulnerable to various security attacks. This paper proposes a federated deep learning-based architecture to detect false data injection attacks (FDIAs) in CPPS. The proposed work offers a strong, decentralized alternative with the ability to boost detection accuracy while maintaining data privacy, presenting a significant opportunity for real-world applications in the smart grid. This framework combines state-of-the-art machine learning and deep learning models, which are used in both centralized and federated learning configurations, to boost the detection of false data injection attacks in cyber-physical power systems. In particular, the research uses a multi-stage detection framework that combines several models, including classic machine learning classifiers like Random Forest and ExtraTrees Classifiers, and deep learning architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results demonstrate that Bidirectional GRU and LSTM models with attention layers in a federated learning setup achieve superior performance, with accuracy approaching 99.8%. This approach enhances both detection accuracy and data privacy, offering a robust solution for FDIA detection in real-world smart grid applications.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en17215337
- OA Status
- gold
- Cited By
- 10
- References
- 32
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4403836530Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en17215337Digital Object Identifier
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Federated Deep Learning Model for False Data Injection Attack Detection in Cyber Physical Power SystemsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
- Publication date
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2024-10-26Full publication date if available
- Authors
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Firdous Kausar, Simone Del Deo, Sajid Hussain, Ziyaul HaqueList of authors in order
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https://doi.org/10.3390/en17215337Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/en17215337Direct OA link when available
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Cyber-physical system, Computer science, Deep learning, Computer security, Power (physics), Artificial intelligence, Operating system, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 10Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(FDIAs) | 57 |
| abstract_inverted_index.Despite | 28 |
| abstract_inverted_index.ability | 70 |
| abstract_inverted_index.achieve | 179 |
| abstract_inverted_index.attacks | 56, 118 |
| abstract_inverted_index.between | 23 |
| abstract_inverted_index.classic | 137 |
| abstract_inverted_index.machine | 94, 138 |
| abstract_inverted_index.models, | 99, 135 |
| abstract_inverted_index.results | 163 |
| abstract_inverted_index.several | 134 |
| abstract_inverted_index.strong, | 65 |
| abstract_inverted_index.systems | 2, 13 |
| abstract_inverted_index.various | 40 |
| abstract_inverted_index.accuracy | 74, 183, 191 |
| abstract_inverted_index.approach | 187 |
| abstract_inverted_index.attacks. | 42 |
| abstract_inverted_index.combines | 92, 133 |
| abstract_inverted_index.electric | 11, 21 |
| abstract_inverted_index.enhances | 188 |
| abstract_inverted_index.learning | 95, 98, 108, 139, 149, 177 |
| abstract_inverted_index.offering | 195 |
| abstract_inverted_index.privacy, | 78, 194 |
| abstract_inverted_index.proposed | 61 |
| abstract_inverted_index.proposes | 45 |
| abstract_inverted_index.research | 126 |
| abstract_inverted_index.security | 41 |
| abstract_inverted_index.solution | 198 |
| abstract_inverted_index.superior | 180 |
| abstract_inverted_index.systems. | 122 |
| abstract_inverted_index.Recurrent | 159 |
| abstract_inverted_index.attention | 172 |
| abstract_inverted_index.benefits, | 30 |
| abstract_inverted_index.detection | 73, 113, 130, 190, 201 |
| abstract_inverted_index.federated | 47, 107, 176 |
| abstract_inverted_index.framework | 91, 131 |
| abstract_inverted_index.including | 136 |
| abstract_inverted_index.injection | 55, 117 |
| abstract_inverted_index.integrate | 4 |
| abstract_inverted_index.ExtraTrees | 145 |
| abstract_inverted_index.Short-Term | 154 |
| abstract_inverted_index.facilitate | 15 |
| abstract_inverted_index.presenting | 79 |
| abstract_inverted_index.real-world | 84, 203 |
| abstract_inverted_index.technology | 8 |
| abstract_inverted_index.vulnerable | 38 |
| abstract_inverted_index.alternative | 67 |
| abstract_inverted_index.approaching | 184 |
| abstract_inverted_index.centralized | 105 |
| abstract_inverted_index.classifiers | 140 |
| abstract_inverted_index.demonstrate | 164 |
| abstract_inverted_index.environment | 34 |
| abstract_inverted_index.information | 5, 19 |
| abstract_inverted_index.maintaining | 76 |
| abstract_inverted_index.multi-stage | 129 |
| abstract_inverted_index.opportunity | 82 |
| abstract_inverted_index.particular, | 124 |
| abstract_inverted_index.significant | 81 |
| abstract_inverted_index.Classifiers, | 146 |
| abstract_inverted_index.applications | 85 |
| abstract_inverted_index.architecture | 50 |
| abstract_inverted_index.conventional | 10 |
| abstract_inverted_index.performance, | 181 |
| abstract_inverted_index.Bidirectional | 166 |
| abstract_inverted_index.applications. | 206 |
| abstract_inverted_index.architectures | 150 |
| abstract_inverted_index.bidirectional | 16 |
| abstract_inverted_index.communication | 7, 17, 33 |
| abstract_inverted_index.decentralized | 66 |
| abstract_inverted_index.Cyber-physical | 0 |
| abstract_inverted_index.cyber-physical | 120 |
| abstract_inverted_index.learning-based | 49 |
| abstract_inverted_index.configurations, | 109 |
| abstract_inverted_index.state-of-the-art | 93 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5016681892, https://openalex.org/A5082200006 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I9254433 |
| citation_normalized_percentile.value | 0.95321093 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |