Application of Artificial Immune Systems for Detection of Network Inclusions
Abstract
The paper discusses the solution to the problem of detecting malicious information using a negative selection algorithm, which is actively used in artificial immune systems (hereinafter referred to as AIS). A computational experiment is presented that demonstrates the system's defensive response when an anomalous object is detected. Some of the principles of artificial immune systems and one of the main methods of their use in relation to computer systems are described. Using a specific example, the application of the negative selection algorithm is considered. A program for monitoring and designing the network infrastructure of a computer system was designed and developed.
In recent years, much attention has been paid to the study of methods for biological modeling of artificial intelligence, such as artificial neural networks and AIS. These methods are one of the most promising approaches to solving problems of anomaly detection, because they operate as closely as possible to the robust biological immune systems of humans.
Negative selection in the immune system is used to recognize foreign antigens by removing those cells (antibodies) that react to self-antigens. This process is called “friend or foe” recognition. The article presents a modified negative selection algorithm and conducted a computational experiment with an immune system that detects network intrusions. A computational experiment demonstrates the system's defensive response when an anomalous object is detected. Using a specific example, the application of the negative selection algorithm is considered.
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