Design of Artificial Immune System - Models and Algorithms

Artificial Immune Systems (AIS) belong to a group of computational intelligence methods inspired by the working mechanisms of biological immune systems to solve various computational problems. Artificial Neural Networks (ANNs) themselves are often used in various fields such as anomaly detection, pattern recognition, cyber and network security, task scheduling, process optimization, and data analysis, with the application of various ANN algorithms. In the AIS approach, there are four basic algorithms that serve as the main foundation, namely the Negative Selection Algorithm (NSA), Artificial Immune Networks (aiNet), Clonal Selection Algorithm (CLONALG), and Dendritic Cell Algorithm (DCA). The problem that occurs at this time is that there is still a lack of papers that discuss the main basic algorithms in AIS, resulting in difficulties in developing new models of basic algorithms. Apart from that, many other aspects of the natural immune system have not been touched due to not yet understanding the basic algorithm of AIS. This paper aims to explain the main models and algorithms in AIS above so that in future research, new algorithms can be developed based on the basic algorithm as a reference. The results of this paper are a review of the main basic models and algorithms in AIS.