Graph knowledge models and ontologies are very powerful modeling and re
asoning tools. We propose an effective approach to model network attacks and
attack prediction which plays important roles in security management. The goals
of this study are: First we model network attacks, their prerequisites and
consequences using knowledge representation methods in order to provide
description logic reasoning and inference over attack domain concepts. And
secondly, we propose an ontology-based system which predicts potential attacks
using inference and observing information which provided by sensory inputs. We
generate our ontology and evaluate corresponding methods using CAPEC, CWE, and
CVE hierarchical datasets. Results from experiments show significant capability
improvements comparing to traditional hierarchical and relational models.
Proposed method also reduces false alarms and improves intrusion detection
effectiveness.Comment: 9 page