Please use this identifier to cite or link to this item:
http://hdl.handle.net/1843/ESBF-AE8R2C
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor1 | Wagner Meira Junior | pt_BR |
dc.contributor.advisor-co1 | Italo Fernando Scota Cunha | pt_BR |
dc.contributor.referee1 | Italo Fernando Scota Cunha | pt_BR |
dc.contributor.referee2 | Cristine Hoepers | pt_BR |
dc.contributor.referee3 | Dorgival Olavo Guedes Neto | pt_BR |
dc.contributor.referee4 | Klaus Steding-jessen | pt_BR |
dc.creator | Osvaldo Luis Henriques de Morais Fonseca | pt_BR |
dc.date.accessioned | 2019-08-11T03:14:38Z | - |
dc.date.available | 2019-08-11T03:14:38Z | - |
dc.date.issued | 2016-03-28 | pt_BR |
dc.identifier.uri | http://hdl.handle.net/1843/ESBF-AE8R2C | - |
dc.description.abstract | Spam messages are often used to propagate malware, to disseminate phishing exploits, and to advertise illegal products. Those messages generate costs for users and network operators, but it is hard to measure how much of their costs are associated with spam traffic, and who actually pays for it. In this work, we provide a method to quantify the transit costs of spam traffic. We issue traceroutes from RIPE Atlas vantage points to estimate the routes traversed by spam messages collected at five honeypots. These collectors simulate vulnerable machines and lead spammers to believe they are interacting with legitimate open relays and proxies. Then we map IP-level traceroute measurements to AS-level paths and use the database of inter-network business relationships to infer the spam traffic costs. Our results show that stub networks are systematically subject to high spam traffic costs and that large ASes can receive twice with the spam traffic of the same message. Furthermore, we show that some networks profit from spam traffic and might not be interested in filtering spam; other networks, even paying for spam traffic, when they can foward these messages to their customers may not be interested in filtering them. Finally, we present a simple but effective algorithm to identify the networks that would benefit in cooperating to filter spam traffic at the origin to reduce transit costs. | pt_BR |
dc.description.resumo | Mensagens de spam são utilizadas na propagação de malware, disseminação de phishing e na divulgação de produtos ilegais. Essas mensagens geram custos para usuários e operadores de rede, porém é difícil mensurar quanto desse custo está associado ao tráfego de spam e quem paga por esse tráfego. Neste trabalho, propusemos uma metodologia para quantificar o custo do tráfego de spam para os operadores de rede. Identificamos as rotas percorridas pelas mensagens de spam capturadas por cinco coletores. Combinando o volume do tráfego de spam, as rotas inferidas e a base de dados de relações entre ASes, mostramos que redes de borda são sistematicamente oneradas. Além disso, mostramos que algumas redes lucram com o tráfego de spam e provavelmente não estão interessadas em filtrar esse tráfego. Finalmente, apresentamos um algoritmo simples mas eficiente para identificar redes que se beneficiariam em cooperar na filtragem de spam para reduzir os custos associados ao tráfego de spam. | pt_BR |
dc.language | Português | pt_BR |
dc.publisher | Universidade Federal de Minas Gerais | pt_BR |
dc.publisher.initials | UFMG | pt_BR |
dc.rights | Acesso Aberto | pt_BR |
dc.subject | Spam | pt_BR |
dc.subject | Técnicas de medição | pt_BR |
dc.subject | Topologia de rede | pt_BR |
dc.subject.other | Telecomunicações Tráfego Custos | pt_BR |
dc.subject.other | Spam (Mensagens eletrônicas) | pt_BR |
dc.subject.other | Computação | pt_BR |
dc.subject.other | Redes de computadores | pt_BR |
dc.title | Medição, caracterização e redução dos custos associados ao tráfego de spam | pt_BR |
dc.type | Dissertação de Mestrado | pt_BR |
Appears in Collections: | Dissertações de Mestrado |
Files in This Item:
File | Description | Size | Format | |
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osvaldoluishenriquesfonseca.pdf | 4.08 MB | Adobe PDF | View/Open |
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