Immune systems in computer science - Ljubič, Karin; Fister Jr. , Iztok; Fister, Iztok (Anali PAZU, 2022)
A simplified description of the immune system is as follows: this is an organic system intended for protecting the host organism from the threats posed to it from pathogens and toxic substances. The architecture of the immune system is such that a series of defensive layers protect the host. Once the pathogen makes it inside the host, it must contend with the innate and adaptive immune system. These two immunological sub-systems are comprised of several types of cells and molecules produced by specialized organs and processes to address the self non-self problem. Artificial Immune Systems (AIS) are computational models, based on natural immune systems. They tend to solve specific problems in computer science by resembling the natural mechanisms. These systems are more widely applied within problem domains like clustering, pattern recognition, classification, optimization, and machine learning. Modern AIS are inspired by one of three sub-fields: clonal selection, negative selection, and immune network algorithms. In computer science, the clonal selection pattern can be used for pattern matching and optimization. The negative selection algorithm was designed for change detection, novelty detection, and intrusion detection. Immune network algorithms are inspired by the immune network theory of the acquired immune system and it is an upgrade of the clonal selection theory. The objective of the immune network process is to prepare a repertoire of discrete pattern detectors for a given problem domain, where better performing solutions suppress low affinity solutions within the same network. This is an interactive process of exposing the pattern to external information to which it responds. This article explains the biological background, the mechanisms of AIS, and presents their real-world applications. It presents an overview of those important applications of AIS for solving problems from problem domains like data analysis, anomaly detection, intrusion detection, and others.
Keywords: natural immune system; artificial immune system; antigen; antibody; algorithm; somatic hyper-mutation