Abstract
The given paper proposes a procedure for detecting network attacks based on a hybrid model that combines deep learning methods and artificial immune systems and increases the efficiency of network traffic analysis. During the development process, the constituent components of a hybrid system for identifying network incidents have been specified with a preceding analysis of existing approaches to its construction. Conceptual architectures of the intrusion detection system have been proposed, functional simulation and data flow simulation for the system comprehensive description have been carried out. Theoretical analysis of the concepts selected for implementation of the development methods of network detection systems has been carried out and the procedures of their hybridization have been substantiated. A software package for comparative analysis of the neuroimmune approach with machine learning methods has been developed and tested.
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