File sharing anomaly detection in business environments
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/28365 |
Resumo: | File sharing is the activity of making archives (documents, videos, photos) available to other users. Enterprises use file sharing to make archives available to their employees or clients. The availability of these files can be done through an internal network, cloud service (external) or even Peer-to-Peer (P2P). Most of the time, the files within the file sharing service have sensitive information that cannot be disclosed. Equifax data breach attack exploited a zero-day attack that allowed arbitrary code execution, leading to a huge data breach as over 143 million user information was presumed compromised. Ransomware is a type of malware that encrypts computer data (documents, media, ...) making it inaccessible to the user, demanding a ransom for the decryption of the data. This type of malware has been a serious threat to enterprises. WannaCry and NotPetya are some examples of ransomware that had a huge impact on enterprises with big amounts of ransoms, for example WannaCry reached more than 142,361.51$ in ransoms. In this dissertation, we purpose a system that can detect file sharing anomalies like ransomware (WannaCry, NotPetya) and theft (Equifax breach), and also their propagation. The solution consists of network monitoring, the creation of communication profiles for each user/machine, an analysis algorithm using machine learning and a countermeasure mechanism in case an anomaly is detected. |
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File sharing anomaly detection in business environmentsNetworkAnomaly detectionFile sharingRansomwareTheftPropagationMachine learningFile sharing is the activity of making archives (documents, videos, photos) available to other users. Enterprises use file sharing to make archives available to their employees or clients. The availability of these files can be done through an internal network, cloud service (external) or even Peer-to-Peer (P2P). Most of the time, the files within the file sharing service have sensitive information that cannot be disclosed. Equifax data breach attack exploited a zero-day attack that allowed arbitrary code execution, leading to a huge data breach as over 143 million user information was presumed compromised. Ransomware is a type of malware that encrypts computer data (documents, media, ...) making it inaccessible to the user, demanding a ransom for the decryption of the data. This type of malware has been a serious threat to enterprises. WannaCry and NotPetya are some examples of ransomware that had a huge impact on enterprises with big amounts of ransoms, for example WannaCry reached more than 142,361.51$ in ransoms. In this dissertation, we purpose a system that can detect file sharing anomalies like ransomware (WannaCry, NotPetya) and theft (Equifax breach), and also their propagation. The solution consists of network monitoring, the creation of communication profiles for each user/machine, an analysis algorithm using machine learning and a countermeasure mechanism in case an anomaly is detected.Partilha de ficheiros é a atividade de disponibilizar ficheiros (documentos, vídeos, fotos) a utilizadores. As empresas usam a partilha de ficheiros para disponibilizar ficheiros aos seus utilizadores e trabalhadores. A disponibilidade destes ficheiros pode ser feita a partir de uma rede interna, serviço de nuvem (externo) ou até Ponto-a-Ponto. Normalmente, os ficheiros contidos no serviço de partilha de ficheiros contêm dados confidenciais que não podem ser divulgados. O ataque de violação de dados realizado a Equifax explorou uma vulnerabilidade de dia zero que permitiu execução de código arbitrário, levando a que a informação de 143 milhões de utilizadores fosse comprometida. Ransomware é um tipo de malware que cifra os dados do computador (documentos, multimédia...) tornando-os inacessíveis ao utilizador, exigindo a este um resgate para decifrar esses dados. Este tipo de malware tem sido uma grande ameaça às empresas atuais. WannaCry e NotPetya são alguns exemplos de Ransomware que tiveram um grande impacto com grandes quantias de resgate, WannaCry alcançou mais de 142,361.51$ em resgates. Neste tabalho, propomos um sistema que consiga detectar anomalias na partilha de ficheiros, como o ransomware (WannaCry, NotPetya) e roubo de dados (violação de dados Equifax), bem como a sua propagação. A solução consiste na monitorização da rede da empresa, na criação de perfis para cada utilizador/máquina, num algoritmo de machine learning para análise dos dados e num mecanismo que bloqueie a máquina afetada no caso de se detectar uma anomalia.2020-05-05T14:30:55Z2018-01-01T00:00:00Z2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/28365TID:202234088engMota, João Manuel Ribeiroinfo: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:RCAAP2024-02-22T11:54:52Zoai:ria.ua.pt:10773/28365Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:00:55.795961Repositório 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 |
File sharing anomaly detection in business environments |
title |
File sharing anomaly detection in business environments |
spellingShingle |
File sharing anomaly detection in business environments Mota, João Manuel Ribeiro Network Anomaly detection File sharing Ransomware Theft Propagation Machine learning |
title_short |
File sharing anomaly detection in business environments |
title_full |
File sharing anomaly detection in business environments |
title_fullStr |
File sharing anomaly detection in business environments |
title_full_unstemmed |
File sharing anomaly detection in business environments |
title_sort |
File sharing anomaly detection in business environments |
author |
Mota, João Manuel Ribeiro |
author_facet |
Mota, João Manuel Ribeiro |
author_role |
author |
dc.contributor.author.fl_str_mv |
Mota, João Manuel Ribeiro |
dc.subject.por.fl_str_mv |
Network Anomaly detection File sharing Ransomware Theft Propagation Machine learning |
topic |
Network Anomaly detection File sharing Ransomware Theft Propagation Machine learning |
description |
File sharing is the activity of making archives (documents, videos, photos) available to other users. Enterprises use file sharing to make archives available to their employees or clients. The availability of these files can be done through an internal network, cloud service (external) or even Peer-to-Peer (P2P). Most of the time, the files within the file sharing service have sensitive information that cannot be disclosed. Equifax data breach attack exploited a zero-day attack that allowed arbitrary code execution, leading to a huge data breach as over 143 million user information was presumed compromised. Ransomware is a type of malware that encrypts computer data (documents, media, ...) making it inaccessible to the user, demanding a ransom for the decryption of the data. This type of malware has been a serious threat to enterprises. WannaCry and NotPetya are some examples of ransomware that had a huge impact on enterprises with big amounts of ransoms, for example WannaCry reached more than 142,361.51$ in ransoms. In this dissertation, we purpose a system that can detect file sharing anomalies like ransomware (WannaCry, NotPetya) and theft (Equifax breach), and also their propagation. The solution consists of network monitoring, the creation of communication profiles for each user/machine, an analysis algorithm using machine learning and a countermeasure mechanism in case an anomaly is detected. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01T00:00:00Z 2018 2020-05-05T14:30:55Z |
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/10773/28365 TID:202234088 |
url |
http://hdl.handle.net/10773/28365 |
identifier_str_mv |
TID:202234088 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
dc.format.none.fl_str_mv |
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 |
instacron_str |
RCAAP |
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RCAAP |
reponame_str |
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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