Intrusion detection with type-2 fuzzy ontologies and similarity measures

Abstract

Intrusions carry a serious security risk for financial institutions. As new intrusion types appear continuously, detection systems have to be designed to be able to identify attacks that have never been experienced before. Insights provided by knowledgeable experts can contribute to a high extent to the identification of these anomalies. Based on a critical review of the relevant literature in intrusion detection and similarity measures of interval-valued fuzzy sets, we propose a framework based on fuzzy ontology and similarity measures to incorporate expert knowledge and represent and make use of imprecise information in the intrusion detection process. As an example we developed a fuzzy ontology based on the intrusion detection needs of a financial institution.