MS IPTV audit collection services
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10451/13934 |
Resumo: | Microsoft Mediaroom Internet Protocol TeleVision (MS IPTV), one of the platforms for digital TV, took television to an all new dimension level. MS IPTV is described as a system where a digital television service is delivered to consumers using the Internet Protocol over a broadband connection. Since the infrastructure started to gain complexity and exposure to a number of new risks, never envisaged situations related to television security started to appear. For this reason, MS IPTV security is not only a great asset, but also a necessity. Nowadays it is mandatory to sharpen the wit to get ahead of attackers, who are always waiting for a breach to compromise our systems. MS IPTV log servers collect information about user and system behavior. However, this information only becomes relevant if it can be queried and analyzed with the purpose of providing high-level understanding about the different patterns. This task must comprise powerful data parsing techniques, since MS IPTV is able to generate close to one terabyte of logs per day. This thesis presents an approach that combines data parsing techniques in order to analyze relevant MS IPTV logs, with the main objective to increase security through the investigation of what type of additional information can be extracted from the server log files of a MS IPTV platform. The thesis focus is on diagnosis, trying to understand if it is possible to determine what type of attacks are being perpetrated against the MS IPTV infrastructure. We propose an approach for discovering attacks, where the application logs are scanned to identify coherent groups of occurrences that we call patterns, which are likely to constitute attacks. Our results showed that our approach achieves good results in discovering potential attacks. Our output results can be integrated into the MS IPTV monitoring system tool SCOM (System Center Operations Manager), which is an additional advantage over the other monitoring and log management systems. |
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MS IPTV audit collection servicesRegular ExpressionsIntegrationAttack PatternsSMCSMS IPTVMicrosoft Mediaroom Internet Protocol TeleVision (MS IPTV), one of the platforms for digital TV, took television to an all new dimension level. MS IPTV is described as a system where a digital television service is delivered to consumers using the Internet Protocol over a broadband connection. Since the infrastructure started to gain complexity and exposure to a number of new risks, never envisaged situations related to television security started to appear. For this reason, MS IPTV security is not only a great asset, but also a necessity. Nowadays it is mandatory to sharpen the wit to get ahead of attackers, who are always waiting for a breach to compromise our systems. MS IPTV log servers collect information about user and system behavior. However, this information only becomes relevant if it can be queried and analyzed with the purpose of providing high-level understanding about the different patterns. This task must comprise powerful data parsing techniques, since MS IPTV is able to generate close to one terabyte of logs per day. This thesis presents an approach that combines data parsing techniques in order to analyze relevant MS IPTV logs, with the main objective to increase security through the investigation of what type of additional information can be extracted from the server log files of a MS IPTV platform. The thesis focus is on diagnosis, trying to understand if it is possible to determine what type of attacks are being perpetrated against the MS IPTV infrastructure. We propose an approach for discovering attacks, where the application logs are scanned to identify coherent groups of occurrences that we call patterns, which are likely to constitute attacks. Our results showed that our approach achieves good results in discovering potential attacks. Our output results can be integrated into the MS IPTV monitoring system tool SCOM (System Center Operations Manager), which is an additional advantage over the other monitoring and log management systems.Casimiro, AntónioRepositório da Universidade de LisboaAntunes, Nuno Filipe Fernandes2012-02-03T10:10:55Z2011-122011-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/13934enginfo: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:RCAAP2023-11-08T15:59:25Zoai:repositorio.ul.pt:10451/13934Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:35:51.595430Repositório 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 |
MS IPTV audit collection services |
title |
MS IPTV audit collection services |
spellingShingle |
MS IPTV audit collection services Antunes, Nuno Filipe Fernandes Regular Expressions Integration Attack Patterns SMCS MS IPTV |
title_short |
MS IPTV audit collection services |
title_full |
MS IPTV audit collection services |
title_fullStr |
MS IPTV audit collection services |
title_full_unstemmed |
MS IPTV audit collection services |
title_sort |
MS IPTV audit collection services |
author |
Antunes, Nuno Filipe Fernandes |
author_facet |
Antunes, Nuno Filipe Fernandes |
author_role |
author |
dc.contributor.none.fl_str_mv |
Casimiro, António Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Antunes, Nuno Filipe Fernandes |
dc.subject.por.fl_str_mv |
Regular Expressions Integration Attack Patterns SMCS MS IPTV |
topic |
Regular Expressions Integration Attack Patterns SMCS MS IPTV |
description |
Microsoft Mediaroom Internet Protocol TeleVision (MS IPTV), one of the platforms for digital TV, took television to an all new dimension level. MS IPTV is described as a system where a digital television service is delivered to consumers using the Internet Protocol over a broadband connection. Since the infrastructure started to gain complexity and exposure to a number of new risks, never envisaged situations related to television security started to appear. For this reason, MS IPTV security is not only a great asset, but also a necessity. Nowadays it is mandatory to sharpen the wit to get ahead of attackers, who are always waiting for a breach to compromise our systems. MS IPTV log servers collect information about user and system behavior. However, this information only becomes relevant if it can be queried and analyzed with the purpose of providing high-level understanding about the different patterns. This task must comprise powerful data parsing techniques, since MS IPTV is able to generate close to one terabyte of logs per day. This thesis presents an approach that combines data parsing techniques in order to analyze relevant MS IPTV logs, with the main objective to increase security through the investigation of what type of additional information can be extracted from the server log files of a MS IPTV platform. The thesis focus is on diagnosis, trying to understand if it is possible to determine what type of attacks are being perpetrated against the MS IPTV infrastructure. We propose an approach for discovering attacks, where the application logs are scanned to identify coherent groups of occurrences that we call patterns, which are likely to constitute attacks. Our results showed that our approach achieves good results in discovering potential attacks. Our output results can be integrated into the MS IPTV monitoring system tool SCOM (System Center Operations Manager), which is an additional advantage over the other monitoring and log management systems. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-12 2011-12-01T00:00:00Z 2012-02-03T10:10:55Z |
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/10451/13934 |
url |
http://hdl.handle.net/10451/13934 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134257602363392 |