Cloud aid - auxílio à prevenção de ataques de canal lateral na nuvem

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Gomes, Ricardo Bianchim
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Santa Maria
Brasil
Ciência da Computação
UFSM
Programa de Pós-Graduação em Ciência da Computação
Centro de Tecnologia
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://repositorio.ufsm.br/handle/1/19043
Resumo: The market of cloud-based technologies has been greatly expanded in this current decade. The propagation of this business and technology model has been providing flexibility in services to its users, mainly in financial costs and computational needs. At the same time, unsolved security breaches like Side Channel Attacks (SCA) imply security threats for the ones that make use of these services. This way, several techniques has been proposed for mitigating this risk, but few of them make use of virtual machine instantiation analysis in Infrastructure as a Service. Even among those which does, the adaptive analysis of instantiation behaviour still seem barely explored. This way, this work presents the development of a security tool with possibility of integration with IaaS controllers through a REST API. This methodology actuates analysing virtual machine instantiation patterns with the objective of preventing the attack during the elaboration of its prerequisite. In order to do that, support vector machines are used to generate a predictor model. Tests using Google Cluster Trace dataset show good quality of the generated model and the viability of detecting possible indications of SCA.