An unsupervised cooperative pattern recognition model to identify anomalous massive SNMP data sending

Authors:
Álvaro Herrero;Emilio Corchado;José Manuel Sáiz
Affiliations:
Department of Civil Engineering, University of Burgos, Spain;Department of Civil Engineering, University of Burgos, Spain;Department of Civil Engineering, University of Burgos, Spain
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

In this paper, we review a visual approach and propose it for analysing computer-network activity, which is based on the use of unsupervised connectionist neural network models and does not rely on any previous knowledge of the data being analysed. The presented Intrusion Detection System (IDS) is used as a method to investigate the traffic which travels along the analysed network, detecting SNMP (Simple Network Management Protocol) anomalous traffic patterns. In this paper we have focused our attention on the study of anomalous situations generated by a MIB (Management Information Base) information transfer.