МЕТОДИКА ПОСТРОЕНИЯ УСТОЙЧИВОЙ СИСТЕМЫ ЗАЩИТЫ НА ОСНОВЕ СОСТЯЗАТЕЛЬНОГО МАШИННОГО ОБУЧЕНИЯ В БЕСПРОВОДНЫХ СЕТЯХ 6G

Abstract

Abstract The purpose of research is to develop the technique of analytical processing of big data of services and applications in the new generation communication networks to detect cybersecurity incidents and build sustainable protection systems based on adversarial machine learning. The methods of research: Analysis of modern methods of machine learning and neural network technologies, synthesis and formalization of algorithms for adversarial attacks on machine learning models. Scientific novelty: a technique for analytical processing of emulated data of services and applications for detecting cybersecurity incidents is presented, which provides a groundwork in the field of research into the security issues of complex intelligent services and applications in the infrastructure of wireless networks of the next generation. The result of research: The article proposes a technique of building a sustainable protection system against adversarial attacks in wireless ad hoc networks of the next generation. The main types of adversarial attacks, including poisoning attacks and evasion attacks, are formalized, and methods for generating adversarial examples on tabular, textual, and visual data are described. Several scenarios were generated and exploratory analysis of datasets was carried out using the DeepMIMO emulator. Potential application problems of binary classification and prediction of signal attenuation between a user and a base station for adversarial attacks are presented. The algorithmization of the processes of building and training a sustainable protection system against adversarial attacks in wireless networks of the next generation is presented on the example of emulated data