37th Annual IEEE Conference on Local Computer Networks (2012)
Clearwater Beach, FL, USA USA
Oct. 22, 2012 to Oct. 25, 2012
Kelton Costa , Department of Computing, UNESP - Univ Estadual Paulista, Brasil
Clayton Pereira , Department of Computing, UNESP - Univ Estadual Paulista, Brasil
Rodrigo Nakamura , Department of Computing, UNESP - Univ Estadual Paulista, Brasil
Joao Papa , Department of Computing, UNESP - Univ Estadual Paulista, Brasil
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.
Accuracy, Intrusion detection, Support vector machines, Pattern recognition, Computer networks, Context, Clustering algorithms
K. Costa, C. Pereira, R. Nakamura and J. Papa, "Intrusion detection in computer networks using Optimum-Path Forest clustering," 37th Annual IEEE Conference on Local Computer Networks(LCN), Clearwater Beach, FL, USA USA, 2012, pp. 128-131.