Using Stacked Generalization for Anomaly Detection
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://repositorio-aberto.up.pt/handle/10216/112506 |
Resumo: | Anomaly Detection is an important research topic nowadays, in which the intention is to find patterns in data that do not not conform to expected behavior. This concept is applicable in a large number of different domains and contexts, such as intrusion detection, fraud detection, medical research and social network analysis.Techniques that have been addressed within this topic are diverse, based on different assumptions about how anomalies manifest themselves within the data and can have different outputs (i.e. a numeric score or a labeled classification).Because of this heterogeneity, every technique is specialized in specific characteristics of the data and may only provide a limited insight on what anomalies exist in a given dataset.Ensemble Learning is process that tries to incorporate the opinions of different learners in order to make a more pondered decision.This process has been successfully applied in the past to supervised learning problems and improvements in performance have been empirically observed.Stacked Generalization is one of these methods, in which a learning algorithm is used to combine the different learners.The intention of this thesis is to research the application of Stacked Generalization to current state-of-the-art Anomaly Detection techniques and determine if this method can lead to a better overall performance.These methods will then be evaluated on well-known publicly available datasets used for benchmarking throughout the literature in Anomaly Detection. |
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Using Stacked Generalization for Anomaly DetectionEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringAnomaly Detection is an important research topic nowadays, in which the intention is to find patterns in data that do not not conform to expected behavior. This concept is applicable in a large number of different domains and contexts, such as intrusion detection, fraud detection, medical research and social network analysis.Techniques that have been addressed within this topic are diverse, based on different assumptions about how anomalies manifest themselves within the data and can have different outputs (i.e. a numeric score or a labeled classification).Because of this heterogeneity, every technique is specialized in specific characteristics of the data and may only provide a limited insight on what anomalies exist in a given dataset.Ensemble Learning is process that tries to incorporate the opinions of different learners in order to make a more pondered decision.This process has been successfully applied in the past to supervised learning problems and improvements in performance have been empirically observed.Stacked Generalization is one of these methods, in which a learning algorithm is used to combine the different learners.The intention of this thesis is to research the application of Stacked Generalization to current state-of-the-art Anomaly Detection techniques and determine if this method can lead to a better overall performance.These methods will then be evaluated on well-known publicly available datasets used for benchmarking throughout the literature in Anomaly Detection.2017-09-192017-09-19T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/112506engMiguel Oliveira Sandiminfo: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:RCAAP2023-11-29T12:47:56Zoai:repositorio-aberto.up.pt:10216/112506Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:27:03.665808Repositório 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 |
Using Stacked Generalization for Anomaly Detection |
title |
Using Stacked Generalization for Anomaly Detection |
spellingShingle |
Using Stacked Generalization for Anomaly Detection Miguel Oliveira Sandim Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
title_short |
Using Stacked Generalization for Anomaly Detection |
title_full |
Using Stacked Generalization for Anomaly Detection |
title_fullStr |
Using Stacked Generalization for Anomaly Detection |
title_full_unstemmed |
Using Stacked Generalization for Anomaly Detection |
title_sort |
Using Stacked Generalization for Anomaly Detection |
author |
Miguel Oliveira Sandim |
author_facet |
Miguel Oliveira Sandim |
author_role |
author |
dc.contributor.author.fl_str_mv |
Miguel Oliveira Sandim |
dc.subject.por.fl_str_mv |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
topic |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
description |
Anomaly Detection is an important research topic nowadays, in which the intention is to find patterns in data that do not not conform to expected behavior. This concept is applicable in a large number of different domains and contexts, such as intrusion detection, fraud detection, medical research and social network analysis.Techniques that have been addressed within this topic are diverse, based on different assumptions about how anomalies manifest themselves within the data and can have different outputs (i.e. a numeric score or a labeled classification).Because of this heterogeneity, every technique is specialized in specific characteristics of the data and may only provide a limited insight on what anomalies exist in a given dataset.Ensemble Learning is process that tries to incorporate the opinions of different learners in order to make a more pondered decision.This process has been successfully applied in the past to supervised learning problems and improvements in performance have been empirically observed.Stacked Generalization is one of these methods, in which a learning algorithm is used to combine the different learners.The intention of this thesis is to research the application of Stacked Generalization to current state-of-the-art Anomaly Detection techniques and determine if this method can lead to a better overall performance.These methods will then be evaluated on well-known publicly available datasets used for benchmarking throughout the literature in Anomaly Detection. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-09-19 2017-09-19T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio-aberto.up.pt/handle/10216/112506 |
url |
https://repositorio-aberto.up.pt/handle/10216/112506 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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.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 |
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1799135576287346689 |