Seleção de variáveis de rede para detecção de intrusão

• Ano de defesa: 2012
• Autor(a) principal: Alves, Victor Machado
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Ciência da Computação; UFSM; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Segurança; Sistemas de detecção de intrusão; Seleção de variáveis; Security; Intrusion detection systems; Feature selection; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/5413

Intrusion Detection Systems are considered important mechanisms to ensure protection for computer networks. However, the information used by these systems should be properly selected, because the accuracy and performance are sensitive to the quality and size of the analyzed data. The selection of variables for Intrusion Detection Systems (IDS) is a key point in the design of IDS. The process of selection of variables, or features, makes the choice of appropriate information by removing irrelevant data that affect the result of detection. However, existing approaches to assist IDS select the variables only once, not adapting behavioral changes. The variation of the network traffic is not so accompanied by these selectors. A strategy for reducing the false alarm rate based on abnormalities in IDS is evaluating whether a same time interval abrupt changes occur in more than one variable network. However, this strategy takes as hypothesis that the variables are related, requiring a prior procedure for variable selection. This paper proposes a dynamic method of selecting variables for network IDS, called SDCorr (Selection by Dynamic Correlation), which operates in the mode filter and as an evaluator uses the Pearson correlation test. The method dynamically adapts to changes in network traffic through the selection of new variables at each iteration with the detector. Therefore allow track changes in data and establish relationships between variables. As a result, it improves the accuracy and performance of the IDS by eliminating unnecessary variables and decreasing the size of the analyzed data.