A Neutrosophic Framework for Artificial Immune Systems

Abstract

Artificial immune systems (AIS) draw inspiration from the mechanisms of the natural immune system. They extract ideas from the functioning of the natural immune system in order to use them to build computer models to solve real-world problems. While traditional AIS models mimic the biological immune system’s capacity to distinguish between self and non-self entities, they often face challenges in environments where data may be incomplete or ambiguous.  In this paper we introduce neutrosophic AIS that includes degrees of truthiness, falsehood and indeterminacy to better address ambiguity within pattern recognition tasks. Within this neutrosophic framework we discuss and redefine main AIS concepts, including self and non-self categorization, clonal selection, negative selection, and immunological memory. A numerical example, from the field of computer security, illustrates the application of the suggested approach in detecting non-self entities. With the addition of neutrosophic logic, the proposed model significantly enhances AIS’s adaptability and pattern recognition capabilities, addressing uncertainties inherent in real-world applications.