ОБНАРУЖЕНИЕ ИНЦИДЕНТОВ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ НА ОСНОВЕ ТЕХНОЛОГИИ НЕЙРОННЫХ СЕТЕЙ

Abstract

Objective: develop model, algorithmic and software for detecting in real time attempts to disrupt the correct functioning of critical information infrastructure systems with neural network technologies. Methods analysis of modern machine learning methods and neural network technologies, synthesis and modeling of correct behavior of programs, algorithmization of learning processes and application of neural networks, experimental studies of developed algorithms and programs on the stand. Study results: The characteristics of machine learning methods and neural network technologies used to detect software and technical impacts and information security incidents are given. The method for solving this problem based on neural networks with LSTM and FFN architectures has been developed. The description of the algorithm and fragments of the software implementation of the method in the programming languages Python3 and Go using Tensorflow and Keras libraries is given. An important advantage of the proposed approach is the possibility of adapting the neural network in the event of a change in the mode and conditions of operation of the system. The results obtained during the experiments indicate the possibility and expediency of using this approach to detect software and technical impacts on critical information infrastructure systems on a time scale close to real with a high level of reliability. Scientific novelty: consists in the application of deep learning technology based on a long-term short-term neural network LSTM, which has the ability to adapt to changing modes and conditions, to solve the problem of detecting signs of a violation of the correct functioning of nodes of information and telecommunications systems in real time.