The Community for Technology Leaders
RSS Icon
Subscribe
Clearwater Beach, FL, USA USA
Oct. 22, 2012 to Oct. 25, 2012
ISBN: 978-1-4673-1565-4
pp: 128-131
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
ABSTRACT
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.
INDEX TERMS
Accuracy, Intrusion detection, Support vector machines, Pattern recognition, Computer networks, Context, Clustering algorithms
CITATION
Kelton Costa, Clayton Pereira, Rodrigo Nakamura, Joao Papa, "Intrusion detection in computer networks using Optimum-Path Forest clustering", LCN, 2012, 38th Annual IEEE Conference on Local Computer Networks, 38th Annual IEEE Conference on Local Computer Networks 2012, pp. 128-131, doi:10.1109/LCN.2012.6423588
18 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool