Intrusion detection in computer networks using optimum-path forest clustering

• Autor(a) principal: Costa, Kelton [UNESP]
• Data de Publicação: 2012
• Outros Autores: Pereira, Clayton [UNESP], Nakamura, Rodrigo [UNESP], Papa, Joao [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/LCN.2012.6423588; http://hdl.handle.net/11449/73827
• topic: Machine learning techniques; Manual labeling; Optimum-path forests; Pattern recognition techniques; Traditional clustering; Unsupervised techniques; Forestry; Intrusion detection; Learning systems; Pattern recognition; Clustering algorithms; Algorithms; Data; Networks; Set

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.