Deep Latent Space Clustering for Detection of Stealthy False Data Injection Attacks against AC State Estimation in Power Systems Article Swipe
Arnab Bhattacharjee
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.19608615
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.19608615
This paper proposes a self-supervised latent space clustering algorithm, called the Deep Latent Space Clustering, for the detection of stealthy false data injection attacks (FDIAs) in smart grids against state estimation algorithms. The stealthy FDI model considered is based on the accurate AC state estimation and is able to bypass conventional bad data detection (BDD) algorithms with ease. The key element of the detection model is a stacked autoencoder network that first undergoes a carefully designed two-step finetuning process, following which a trainable clustering head is stacked on top of the finetuned encoder and the final network is further trained to achieve a clean clustering of the data into benign and compromised samples without labelled supervision. To test the efficacy and scalability of the detection model, it is tested on the standard IEEE 14 bus, 118 bus and 300 bus test systems. The self-supervised clustering model is compared to several supervised, semi-supervised and unsupervised algorithms proposed in the literature for detection of FDI on the aforementioned test cases and has been found to perform at par with the state of the art among them.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.36227/techrxiv.19608615
- https://www.techrxiv.org/articles/preprint/Deep_Latent_Space_Clustering_for_Detection_of_Stealthy_False_Data_Injection_Attacks_against_AC_State_Estimation_in_Power_Systems/19608615/1/files/34829169.pdf
- OA Status
- gold
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283384849
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- OpenAlex ID
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https://openalex.org/W4283384849Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.36227/techrxiv.19608615Digital Object Identifier
- Title
-
Deep Latent Space Clustering for Detection of Stealthy False Data Injection Attacks against AC State Estimation in Power SystemsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-04-21Full publication date if available
- Authors
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Arnab BhattacharjeeList of authors in order
- Landing page
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https://doi.org/10.36227/techrxiv.19608615Publisher landing page
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https://www.techrxiv.org/articles/preprint/Deep_Latent_Space_Clustering_for_Detection_of_Stealthy_False_Data_Injection_Attacks_against_AC_State_Estimation_in_Power_Systems/19608615/1/files/34829169.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.techrxiv.org/articles/preprint/Deep_Latent_Space_Clustering_for_Detection_of_Stealthy_False_Data_Injection_Attacks_against_AC_State_Estimation_in_Power_Systems/19608615/1/files/34829169.pdfDirect OA link when available
- Concepts
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Cluster analysis, Computer science, Autoencoder, Scalability, Data mining, Artificial intelligence, State (computer science), Pattern recognition (psychology), Machine learning, Artificial neural network, Algorithm, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.clustering | 7, 83, 104, 144 |
| abstract_inverted_index.considered | 36 |
| abstract_inverted_index.estimation | 30, 44 |
| abstract_inverted_index.finetuning | 77 |
| abstract_inverted_index.literature | 158 |
| abstract_inverted_index.Clustering, | 14 |
| abstract_inverted_index.algorithms. | 31 |
| abstract_inverted_index.autoencoder | 68 |
| abstract_inverted_index.compromised | 111 |
| abstract_inverted_index.scalability | 121 |
| abstract_inverted_index.supervised, | 150 |
| abstract_inverted_index.conventional | 50 |
| abstract_inverted_index.supervision. | 115 |
| abstract_inverted_index.unsupervised | 153 |
| abstract_inverted_index.aforementioned | 165 |
| abstract_inverted_index.self-supervised | 4, 143 |
| abstract_inverted_index.semi-supervised | 151 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5018375430 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I160993911, https://openalex.org/I165143802, https://openalex.org/I68891433 |
| citation_normalized_percentile.value | 0.07014682 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |