Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs

Detalhes bibliográficos
Autor(a) principal: Silva, Anderson A. A. [UNESP]
Data de Publicação: 2014
Outros Autores: Pontes, Elvis, Zhou, Fen, Kofuji, Sergio Takeo, IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/183950
Resumo: Nodes positioning is an essential issue for diverse applications in Mobile Ad Hoc Networks (MANETs). However, besides misbehaving nodes that could cause power depletion, MANETs are also susceptible to cyber-attacks, which can make the network unstable and/or unavailable. Therefore, considering the gaps aforementioned, the goal of this paper is to propose a model for identifying malicious/misbehaving nodes by: (1) the use of two forecasting methods (Grey Model and Polynomial Regression); (2) variability analysis; and (3) simulation of fake node positions. The obtained results allow concluding our model has high rate of accuracy for detecting malicious/misbehaving nodes.
id UNSP_08b8f2aa840df8188877c2b7675d9903
oai_identifier_str oai:repositorio.unesp.br:11449/183950
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETsGrey Theory GM(1,1)Polynomial RegressionMANETmisbehaving node detectionmalicious node identificationprediction modelNodes positioning is an essential issue for diverse applications in Mobile Ad Hoc Networks (MANETs). However, besides misbehaving nodes that could cause power depletion, MANETs are also susceptible to cyber-attacks, which can make the network unstable and/or unavailable. Therefore, considering the gaps aforementioned, the goal of this paper is to propose a model for identifying malicious/misbehaving nodes by: (1) the use of two forecasting methods (Grey Model and Polynomial Regression); (2) variability analysis; and (3) simulation of fake node positions. The obtained results allow concluding our model has high rate of accuracy for detecting malicious/misbehaving nodes.Univ Sao Paulo, LSI POLI, BR-05508 Sao Paulo, BrazilUniv Avignon, CERI LIA, Avignon, FranceUniv Estadual Paulista, Unip, Sao Paulo, BrazilUniv Estadual Paulista, Unip, Sao Paulo, BrazilIeeeUniversidade de São Paulo (USP)Univ AvignonUniversidade Estadual Paulista (Unesp)Silva, Anderson A. A. [UNESP]Pontes, ElvisZhou, FenKofuji, Sergio TakeoIEEE2019-10-03T18:18:36Z2019-10-03T18:18:36Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject162-1682014 Ieee Global Communications Conference (globecom 2014). New York: Ieee, p. 162-168, 2014.2334-0983http://hdl.handle.net/11449/183950WOS:000369900400027Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2014 Ieee Global Communications Conference (globecom 2014)info:eu-repo/semantics/openAccess2021-10-23T15:54:45Zoai:repositorio.unesp.br:11449/183950Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:38:09.437457Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs
title Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs
spellingShingle Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs
Silva, Anderson A. A. [UNESP]
Grey Theory GM(1,1)
Polynomial Regression
MANET
misbehaving node detection
malicious node identification
prediction model
title_short Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs
title_full Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs
title_fullStr Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs
title_full_unstemmed Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs
title_sort Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs
author Silva, Anderson A. A. [UNESP]
author_facet Silva, Anderson A. A. [UNESP]
Pontes, Elvis
Zhou, Fen
Kofuji, Sergio Takeo
IEEE
author_role author
author2 Pontes, Elvis
Zhou, Fen
Kofuji, Sergio Takeo
IEEE
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Univ Avignon
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Silva, Anderson A. A. [UNESP]
Pontes, Elvis
Zhou, Fen
Kofuji, Sergio Takeo
IEEE
dc.subject.por.fl_str_mv Grey Theory GM(1,1)
Polynomial Regression
MANET
misbehaving node detection
malicious node identification
prediction model
topic Grey Theory GM(1,1)
Polynomial Regression
MANET
misbehaving node detection
malicious node identification
prediction model
description Nodes positioning is an essential issue for diverse applications in Mobile Ad Hoc Networks (MANETs). However, besides misbehaving nodes that could cause power depletion, MANETs are also susceptible to cyber-attacks, which can make the network unstable and/or unavailable. Therefore, considering the gaps aforementioned, the goal of this paper is to propose a model for identifying malicious/misbehaving nodes by: (1) the use of two forecasting methods (Grey Model and Polynomial Regression); (2) variability analysis; and (3) simulation of fake node positions. The obtained results allow concluding our model has high rate of accuracy for detecting malicious/misbehaving nodes.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2019-10-03T18:18:36Z
2019-10-03T18:18:36Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv 2014 Ieee Global Communications Conference (globecom 2014). New York: Ieee, p. 162-168, 2014.
2334-0983
http://hdl.handle.net/11449/183950
WOS:000369900400027
identifier_str_mv 2014 Ieee Global Communications Conference (globecom 2014). New York: Ieee, p. 162-168, 2014.
2334-0983
WOS:000369900400027
url http://hdl.handle.net/11449/183950
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2014 Ieee Global Communications Conference (globecom 2014)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 162-168
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808128391330660352

Registros relacionados