Advertising: Machine Learning algorithms to detect anomalies

Detalhes bibliográficos
Autor(a) principal: Rusinova, Valentyna
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/10362/145476
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
id RCAP_8cdfe5e292399773634d50f3ee731b3b
oai_identifier_str oai:run.unl.pt:10362/145476
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Advertising: Machine Learning algorithms to detect anomaliesMachine LearningDigital ForensicsCybercrimeCybersecurityAdvertisingUBlockDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceSecurity is a major worry concern nowadays, as the technological development has led to an excessive use of digital devices, where everyone surfs web pages, social networks, blogs, etc. The internet is currently where people spend most of their free time, where they search for and place their information. However, many individuals take advantage of this information for malicious purposes, such as identity, and bank account theft, or even to compromise documents. Furthermore, the methods used not only cause damage to individuals but also affect electronic devices, which become infected with malware, and often become impossible to use again. Consequently, people are becoming progressively worried about digital security, which pushes them to increasingly use software that blocks all kinds of malware. But for the development of Digital Marketing, overcoming these negative consequences becomes a real challenge, as it is affected by ad-blocking software, since most malware is embedded in advertisements, of which criminal minds try to take advantage of. Hence, to overcome the cyber-attacks that can arise from malicious advertisements and prevent internet users from using ad blockers, Digital Marketing will have to find strategies to develop security techniques. With the help of Digital Forensic Science, it is possible to conduct investigations to solve the problems related to digital crime. The expansion of cybersecurity allowed to develop a web extension with which it is possible to block malicious ads, whilst simultaneously allowing for digital advertising not to vanish, but to continue evolving ensuring possible the dissemination of products and information, on which all individuals depend.Santos, Vitor Manuel Pereira Duarte dosRUNRusinova, Valentyna2022-11-14T14:56:51Z2022-10-242022-10-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/145476TID:203097254enginfo: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:RCAAP2024-03-11T05:25:52Zoai:run.unl.pt:10362/145476Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:05.854456Repositório 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 Advertising: Machine Learning algorithms to detect anomalies
title Advertising: Machine Learning algorithms to detect anomalies
spellingShingle Advertising: Machine Learning algorithms to detect anomalies
Rusinova, Valentyna
Machine Learning
Digital Forensics
Cybercrime
Cybersecurity
Advertising
UBlock
title_short Advertising: Machine Learning algorithms to detect anomalies
title_full Advertising: Machine Learning algorithms to detect anomalies
title_fullStr Advertising: Machine Learning algorithms to detect anomalies
title_full_unstemmed Advertising: Machine Learning algorithms to detect anomalies
title_sort Advertising: Machine Learning algorithms to detect anomalies
author Rusinova, Valentyna
author_facet Rusinova, Valentyna
author_role author
dc.contributor.none.fl_str_mv Santos, Vitor Manuel Pereira Duarte dos
RUN
dc.contributor.author.fl_str_mv Rusinova, Valentyna
dc.subject.por.fl_str_mv Machine Learning
Digital Forensics
Cybercrime
Cybersecurity
Advertising
UBlock
topic Machine Learning
Digital Forensics
Cybercrime
Cybersecurity
Advertising
UBlock
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2022
dc.date.none.fl_str_mv 2022-11-14T14:56:51Z
2022-10-24
2022-10-24T00:00:00Z
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/10362/145476
TID:203097254
url http://hdl.handle.net/10362/145476
identifier_str_mv TID:203097254
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
repository.mail.fl_str_mv
_version_ 1799138112912228352

Registros relacionados