Please use this identifier to cite or link to this item: http://hdl.handle.net/10216/106919
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dc.creatorPedro Manuel Vasconcelos Fonseca
dc.date.accessioned2018-03-14T17:53:08Z-
dc.date.available2018-03-14T17:53:08Z-
dc.date.issued2017-07-05
dc.date.submitted2017-07-25
dc.identifier.other208242
dc.identifier.urihttp://hdl.handle.net/10216/106919-
dc.descriptionNo mundo de hoje, as redes Wi-Fi estão difundidas por toda a parte e interligadas ao nosso dia-a-dia: acedemos-lhes através dos nossos smartphones, tablets e telemóveis. Isto significa que inevitavelmente muitos dados sensíveis são transmitidos através destas redes. A encriptação, que está presente em virtualmente todas as redes Wi-Fi, ajuda a proteger estes dados. Ainda assim, através de ataques side-channel é possível obter informação e potencialmente quebrar a privacidade de um utilizador. Esta tese tem o objetivo de desenvolver um destes ataques, de forma a identificar o website a que um utilizador está a aceder. Um dataset sólido, consistindo em numerosas capturas relativas a websites específicos, providencia os meios para que um algoritmo baseado em machine learning classifique uma captura de tráfego. Outro objetivo é estudar o efeito do ruído ao nível da rede na eficácia do algoritmo.
dc.description.abstractIn today's world, Wi-Fi networks are widespread and intertwined with our daily lives: we access them through our smartphones, our tablets and our laptops. This means that inevitably a lot of sensitive data is transferred through these networks. Encryption, which is present in virtually every Wi-Fi network, helps protect this data. Nonetheless, through side-channel attacks it is possible to obtain information and potentially breach a user's privacy. This thesis has the objective of devising such an attack, in order to identify the website a given user is accessing. A solid dataset, consisting of numerous captures pertaining to a specific website, provides the means for an algorithm based on machine learning to classify a traffic capture. Another objective is to study the effect of network-level noise on the accuracy of the algorithm.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectEngenharia electrotécnica, electrónica e informática
dc.subjectElectrical engineering, Electronic engineering, Information engineering
dc.titlePerformance of side-channel attacks on encrypted 802.11 web traffic and impact of network-level noise
dc.typeDissertação
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.tid201802279
dc.subject.fosCiências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
dc.subject.fosEngineering and technology::Electrical engineering, Electronic engineering, Information engineering
thesis.degree.disciplineMestrado Integrado em Engenharia Electrotécnica e de Computadores
thesis.degree.grantorFaculdade de Engenharia
thesis.degree.grantorUniversidade do Porto
thesis.degree.level1
Appears in Collections:FEUP - Dissertação

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