Conference Paper

Towards Ontology-Based Intelligent Model for Intrusion Detection and Prevention

DOI: 10.1007/978-3-642-04091-7_14 Conference: Computational Intelligence in Security for Information Systems - CISIS'09, 2nd International Workshop, Burgos, Spain, 23-26 September 2009 Proceedings
Source: DBLP


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.

KeywordsOntology-Intelligence Security-Intrusion Prevention-Multi-agent systems

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    ABSTRACT: This paper proposes an ontology model for representing intrusion detection events and prevention rules, integrating multiagent systems based on unsupervised and supervised techniques for classification, correlation and pattern recognition. The semantic model describes attacks signatures, reaction tasks, axioms with alerts communication and correlation; nevertheless we have developed the prevention architecture integrated with another security tools. This article focuses on the approach to incorporate semantic operations that facilitate alerts correlation process and providing the inference and reasoning to the ontology model.
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