Network anomalies detection via event analysis and correlation by a smart system
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/27448 |
Resumo: | The multidisciplinary of contemporary societies compel us to look at Information Technology (IT) systems as one of the most significant grants that we can remember. However, its increase implies a mandatory security force for users, a force in the form of effective and robust tools to combat cybercrime to which users, individual or collective, are ex-posed almost daily. Monitoring and detection of this kind of problem must be ensured in real-time, allowing companies to intervene fruitfully, quickly and in unison. The proposed framework is based on an organic symbiosis between credible, affordable, and effective open-source tools for data analysis, relying on Security Information and Event Management (SIEM), Big Data and Machine Learning (ML) techniques commonly applied for the development of real-time monitoring systems. Dissecting this framework, it is composed of a system based on SIEM methodology that provides monitoring of data in real-time and simultaneously saves the information, to assist forensic investigation teams. Secondly, the application of the Big Data concept is effective in manipulating and organising the flow of data. Lastly, the use of ML techniques that help create mechanisms to detect possible attacks or anomalies on the network. This framework is intended to provide a real-time analysis application in the institution ISCTE – Instituto Universitário de Lisboa (Iscte), offering a more complete, efficient, and secure monitoring of the data from the different devices comprising the network. |
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Network anomalies detection via event analysis and correlation by a smart systemData monitoringAnomaly detectionAttack detectionSIEMBig dataML algorithmsMonitorização de dadosDeteção de anomaliasDeteção de ataquesSistemas SIEMAlgoritmos MLThe multidisciplinary of contemporary societies compel us to look at Information Technology (IT) systems as one of the most significant grants that we can remember. However, its increase implies a mandatory security force for users, a force in the form of effective and robust tools to combat cybercrime to which users, individual or collective, are ex-posed almost daily. Monitoring and detection of this kind of problem must be ensured in real-time, allowing companies to intervene fruitfully, quickly and in unison. The proposed framework is based on an organic symbiosis between credible, affordable, and effective open-source tools for data analysis, relying on Security Information and Event Management (SIEM), Big Data and Machine Learning (ML) techniques commonly applied for the development of real-time monitoring systems. Dissecting this framework, it is composed of a system based on SIEM methodology that provides monitoring of data in real-time and simultaneously saves the information, to assist forensic investigation teams. Secondly, the application of the Big Data concept is effective in manipulating and organising the flow of data. Lastly, the use of ML techniques that help create mechanisms to detect possible attacks or anomalies on the network. This framework is intended to provide a real-time analysis application in the institution ISCTE – Instituto Universitário de Lisboa (Iscte), offering a more complete, efficient, and secure monitoring of the data from the different devices comprising the network.A multidisciplinaridade das sociedades contemporâneas obriga-nos a perspetivar os sistemas informáticos como uma das maiores dádivas de que há memória. Todavia o seu incremento implica uma mandatária força de segurança para utilizadores, força essa em forma de ferramentas eficazes e robustas no combate ao cibercrime a que os utilizadores, individuais ou coletivos, são sujeitos quase diariamente. A monitorização e deteção deste tipo de problemas tem de ser assegurada em tempo real, permitindo assim, às empresas intervenções frutuosas, rápidas e em uníssono. A framework proposta é alicerçada numa simbiose orgânica entre ferramentas open source credíveis, acessíveis pecuniariamente e eficazes na monitorização de dados, recorrendo a um sistema baseado em técnicas de Security Information and Event Management (SIEM), Big Data e Machine Learning (ML) comumente aplicadas para a criação de sistemas de monitorização em tempo real. Dissecando esta framework, é composta pela metodologia SIEM que possibilita a monitorização de dados em tempo real e em simultâneo guardar a informação, com o objetivo de auxiliar as equipas de investigação forense. Em segundo lugar, a aplicação do conceito Big Data eficaz na manipulação e organização do fluxo dos dados. Por último, o uso de técnicas de ML que ajudam a criação de mecanismos de deteção de possíveis ataques ou anomalias na rede. Esta framework tem como objetivo uma aplicação de análise em tempo real na instituição ISCTE – Instituto Universitário de Lisboa (Iscte), apresentando uma monitorização mais completa, eficiente e segura dos dados dos diversos dispositivos presentes na mesma.2023-01-25T12:56:20Z2022-12-21T00:00:00Z2022-12-212022-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10071/27448TID:203175387engCruz, Gonçalo Monteiroinfo: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:RCAAP2023-11-09T17:33:40Zoai:repositorio.iscte-iul.pt:10071/27448Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:15:11.371761Repositório 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 |
Network anomalies detection via event analysis and correlation by a smart system |
title |
Network anomalies detection via event analysis and correlation by a smart system |
spellingShingle |
Network anomalies detection via event analysis and correlation by a smart system Cruz, Gonçalo Monteiro Data monitoring Anomaly detection Attack detection SIEM Big data ML algorithms Monitorização de dados Deteção de anomalias Deteção de ataques Sistemas SIEM Algoritmos ML |
title_short |
Network anomalies detection via event analysis and correlation by a smart system |
title_full |
Network anomalies detection via event analysis and correlation by a smart system |
title_fullStr |
Network anomalies detection via event analysis and correlation by a smart system |
title_full_unstemmed |
Network anomalies detection via event analysis and correlation by a smart system |
title_sort |
Network anomalies detection via event analysis and correlation by a smart system |
author |
Cruz, Gonçalo Monteiro |
author_facet |
Cruz, Gonçalo Monteiro |
author_role |
author |
dc.contributor.author.fl_str_mv |
Cruz, Gonçalo Monteiro |
dc.subject.por.fl_str_mv |
Data monitoring Anomaly detection Attack detection SIEM Big data ML algorithms Monitorização de dados Deteção de anomalias Deteção de ataques Sistemas SIEM Algoritmos ML |
topic |
Data monitoring Anomaly detection Attack detection SIEM Big data ML algorithms Monitorização de dados Deteção de anomalias Deteção de ataques Sistemas SIEM Algoritmos ML |
description |
The multidisciplinary of contemporary societies compel us to look at Information Technology (IT) systems as one of the most significant grants that we can remember. However, its increase implies a mandatory security force for users, a force in the form of effective and robust tools to combat cybercrime to which users, individual or collective, are ex-posed almost daily. Monitoring and detection of this kind of problem must be ensured in real-time, allowing companies to intervene fruitfully, quickly and in unison. The proposed framework is based on an organic symbiosis between credible, affordable, and effective open-source tools for data analysis, relying on Security Information and Event Management (SIEM), Big Data and Machine Learning (ML) techniques commonly applied for the development of real-time monitoring systems. Dissecting this framework, it is composed of a system based on SIEM methodology that provides monitoring of data in real-time and simultaneously saves the information, to assist forensic investigation teams. Secondly, the application of the Big Data concept is effective in manipulating and organising the flow of data. Lastly, the use of ML techniques that help create mechanisms to detect possible attacks or anomalies on the network. This framework is intended to provide a real-time analysis application in the institution ISCTE – Instituto Universitário de Lisboa (Iscte), offering a more complete, efficient, and secure monitoring of the data from the different devices comprising the network. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-21T00:00:00Z 2022-12-21 2022-10 2023-01-25T12:56:20Z |
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 |
http://hdl.handle.net/10071/27448 TID:203175387 |
url |
http://hdl.handle.net/10071/27448 |
identifier_str_mv |
TID:203175387 |
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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1799134709047885824 |