A novel method for anomaly detection using beta hebbian learning and principal component analysis
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/25246 |
Resumo: | In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high. |
id |
RCAP_2c55749e6be79b39fe362cc688d91ca6 |
---|---|
oai_identifier_str |
oai:bibliotecadigital.ipb.pt:10198/25246 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
A novel method for anomaly detection using beta hebbian learning and principal component analysisOne-classDimensional reductionBHLPCAIn this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high.CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund and the Secretaría Xeral de Universidades (ref. ED431G 2019/01).Oxford AcademicBiblioteca Digital do IPBZayas-Gato, FranciscoMichelena, ÁlvaroQuintián, HéctorJove, EstebanCasteleiro-Roca, José-LuisLeitão, PauloCalvo-Rolle, Jose Luis2022-03-17T13:58:30Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/25246engZayas-Gato, Francisco; Michelena, Álvaro; Quintián, Héctor; Jove, Esteban; Casteleiro-Roca, José-Luis; Leitão, Paulo; Calvo-Rolle, Jose Luis (2023). A novel method for anomaly detection using beta Hebbian learning and principal component analysis. Logic Journal of the IGPL. eISSN 1368-9894. 31:2, p.390-3991367-075110.1093/jigpal/jzac0261368-9894info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-20T01:18:24Zoai:bibliotecadigital.ipb.pt:10198/25246Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:15:56.043454Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A novel method for anomaly detection using beta hebbian learning and principal component analysis |
title |
A novel method for anomaly detection using beta hebbian learning and principal component analysis |
spellingShingle |
A novel method for anomaly detection using beta hebbian learning and principal component analysis Zayas-Gato, Francisco One-class Dimensional reduction BHL PCA |
title_short |
A novel method for anomaly detection using beta hebbian learning and principal component analysis |
title_full |
A novel method for anomaly detection using beta hebbian learning and principal component analysis |
title_fullStr |
A novel method for anomaly detection using beta hebbian learning and principal component analysis |
title_full_unstemmed |
A novel method for anomaly detection using beta hebbian learning and principal component analysis |
title_sort |
A novel method for anomaly detection using beta hebbian learning and principal component analysis |
author |
Zayas-Gato, Francisco |
author_facet |
Zayas-Gato, Francisco Michelena, Álvaro Quintián, Héctor Jove, Esteban Casteleiro-Roca, José-Luis Leitão, Paulo Calvo-Rolle, Jose Luis |
author_role |
author |
author2 |
Michelena, Álvaro Quintián, Héctor Jove, Esteban Casteleiro-Roca, José-Luis Leitão, Paulo Calvo-Rolle, Jose Luis |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Zayas-Gato, Francisco Michelena, Álvaro Quintián, Héctor Jove, Esteban Casteleiro-Roca, José-Luis Leitão, Paulo Calvo-Rolle, Jose Luis |
dc.subject.por.fl_str_mv |
One-class Dimensional reduction BHL PCA |
topic |
One-class Dimensional reduction BHL PCA |
description |
In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-03-17T13:58:30Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/25246 |
url |
http://hdl.handle.net/10198/25246 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Zayas-Gato, Francisco; Michelena, Álvaro; Quintián, Héctor; Jove, Esteban; Casteleiro-Roca, José-Luis; Leitão, Paulo; Calvo-Rolle, Jose Luis (2023). A novel method for anomaly detection using beta Hebbian learning and principal component analysis. Logic Journal of the IGPL. eISSN 1368-9894. 31:2, p.390-399 1367-0751 10.1093/jigpal/jzac026 1368-9894 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Oxford Academic |
publisher.none.fl_str_mv |
Oxford Academic |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799135442798379008 |