Towards ontology-based intelligent model for intrusion detection and prevention

Abstract

Nowadays new intelligent techniques have been used to improve the intrusion detection process in distributed environments. This paper presents an approach to define an ontology model for representing intrusion detection and prevention events as well as a hybrid intelligent system based on clustering and Artificial Neuronal Networks for classification and pattern recognition. We have specified attacks signatures, reaction rules, asserts, axioms using Ontology Web Language with Description Logic (OWL-DL) with event communication and correlation integrated on Multi-Agent Systems, incorporating supervised and unsupervised models and generating intelligent reasoning. © Springer-Verlag Berlin Heidelberg 2009.