In this paper, we suggest a flexible type-2 fuzzy set algorithm for analysing anomalous behavior trends of some system parameters. This algorithm can be implemented in a performance-based Artificial Immune System (AIS) and used as anomalous behavior recognition engine for a biological-inspired Intrusion Detection System (IDS). The suggested algorithm is based on the idea that real-world applications have the necessity of providing a strong, reliable discrimination between normal and abnormal behaviors but such discrimination is not always well-defined. This fact introduces many degrees of uncertainties in rule-based systems and convinced us to implement a type-2 fuzzy set algorithm that can easily manipulate and minimize the effect of uncertainties in our system.
A Type-2 Fuzzy Set Recognition Algorithm for Artificial Immune Systems / A. Visconti, H. Tahayori - In: Hybrid Artificial Intelligence Systems / [a cura di] E. Corchado, A. Abraham, W. Pedrycz. - Berlin : Springer, 2008. - ISBN 9783540876557. - pp. 491-498 (( Intervento presentato al 3. convegno HAIS tenutosi a Burgos nel 2008.
A Type-2 Fuzzy Set Recognition Algorithm for Artificial Immune Systems
A. ViscontiPrimo
;H. TahayoriUltimo
2008
Abstract
In this paper, we suggest a flexible type-2 fuzzy set algorithm for analysing anomalous behavior trends of some system parameters. This algorithm can be implemented in a performance-based Artificial Immune System (AIS) and used as anomalous behavior recognition engine for a biological-inspired Intrusion Detection System (IDS). The suggested algorithm is based on the idea that real-world applications have the necessity of providing a strong, reliable discrimination between normal and abnormal behaviors but such discrimination is not always well-defined. This fact introduces many degrees of uncertainties in rule-based systems and convinced us to implement a type-2 fuzzy set algorithm that can easily manipulate and minimize the effect of uncertainties in our system.File | Dimensione | Formato | |
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