An Optimum-Path Forest framework for intrusion detection in computer networks

Autor(es)

Pereira, Clayton R. ^[1] ; Nakamura, Rodrigo Y. M. ^[1] ; Costa, Kelton A. P. ^[1] ; Papa, Joao P. ^[1]

Afiliação do(s) autor(es)

^[1] UNESP Univ Estadual Paulista, Dept Comp, Bauru - Brazil

• Tipo de documento: Artigo Científico
• Fonte: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE; v. 25, n. 6, p. 1226-1234, SEP 2012.
• Citações Web of Science: 23

Resumo

Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In order to overcome such limitations, we have introduced a new pattern recognition technique called optimum-path forest (OPF) to this task. Our proposal is composed of three main contributions: to apply OPF for intrusion detection, to identify redundancy in some public datasets and also to perform feature selection over them. The experiments have been carried out on three datasets aiming to compare OPF against Support Vector Machines, Self Organizing Maps and a Bayesian classifier. We have showed that OPF has been the fastest classifier and the always one with the top results. Thus, it can be a suitable tool to detect intrusions on computer networks, as well as to allow the algorithm to learn new attacks faster than other techniques. (C) 2012 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 10/11676-7 - Redução do Tamanho do Conjunto de Treinamento por Floresta de Caminhos Ótimos e sua Aplicação em Máquinas de Vetores de Suporte
• Beneficiário: Rodrigo Yuji Mizobe Nakamura
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 10/02045-3 - Detecção de Intrusões Baseada em Floresta de Caminhos Ótimos
• Beneficiário: Clayton Reginaldo Pereira
• Modalidade de apoio: Bolsas no Brasil - Mestrado