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.
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Visconti, A., Tahayori, H. (2008). A Type-2 Fuzzy Set Recognition Algorithm for Artificial Immune Systems. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_61
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DOI: https://doi.org/10.1007/978-3-540-87656-4_61
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