The hypothesis of achieving technological singularity through a quasi-biological paradigm of describing intelligent IDS

In a general effort to achieve a quasi-biological consensus for an IT solution within Industry 5.0, it was decided to hypothesize the achievement of a technological singularity through a quasi-biological paradigm for describing intelligent IDS, which establishes a special "digital DNA" for information security systems in two-phase modes of operation of batch neural network programs.

The development of quasi-biologically inspired algorithms and technologies that will mimic the work of quasi-biological systems, such as the immune system or neural networks, additionally equips these systems with learnable and self-learning modules capable of adapting to new threats and changes in the environment. Evolutionary selective algorithms enhance the process of detecting and adapting to new types of threats. One of the concepts of the development of evolutionary selective algorithms in intelligent IDS is the use of genetic programming to search for optimal parameters and threat detection structures. This allows IDS to learn from existing data and automatically adapt to new threats, which increases its effectiveness and accuracy. The use of multi-population methods of evolutionary optimi-zation (clonal selection, genetic algorithms, etc.) to create various groups of algorithms for detecting attacks on PA, each of which may specialize in a specific type of threat, increases the overall reliabil-ity of the system and reduces the likelihood of false positives. The properties of the quasi-biological paradigm, such as adaptivity, self-organization, and emergent behavior, can be successfully preserved and applied in the development of intelligent IDS within the framework of the Industry 5.0 concept.