Graph-based feature enrichment for online intrusion detection in virtual networks

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Sanz, Igor Jochem
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal do Rio de Janeiro
Brasil
Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia
Programa de Pós-Graduação em Engenharia Elétrica
UFRJ
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://hdl.handle.net/11422/11566
Resumo: The increasing number of connected devices to provide the required ubiquitousness of Internet of Things paves the way for distributed network attacks at an unprecedented scale. Graph theory, strengthened by machine learning techniques, improves an automatic discovery of group behavior patterns of network threats often omitted by traditional security systems. Furthermore, Network Function Virtualization is an emergent technology that accelerates the provisioning of on-demand security function chains tailored to an application. Therefore, repeatable compliance tests and performance comparison of such function chains are mandatory. The contributions of this dissertation are divided in two parts. First, we propose an intrusion detection system for online threat detection enriched by a graph-learning analysis. We develop a feature enrichment algorithm that infers metrics from a graph analysis. By using different machine learning techniques, we evaluated our algorithm for three network traffic datasets. We show that the proposed graph-based enrichment improves the threat detection accuracy up to 15.7% and significantly reduces the false positives rate. Second, we aim to evaluate intrusion detection systems deployed as virtual network functions. Therefore, we propose and develop SFCPerf, a framework for an automatic performance evaluation of service function chaining. To demonstrate SFCPerf functionality, we design and implement a prototype of a security service function chain, composed of our intrusion detection system and a firewall. We show the results of a SFCPerf experiment that evaluates the chain prototype on top of the open platform for network function virtualization (OPNFV).