The growing popularity of large language models in various fields of scientific and industrial activity leads to the emergence of solutions using these technologies for completely different tasks. This article suggests using the BERT, GPT, and GPT-2 language models to detect malicious code. The neural network model, previously trained on natural texts, is further trained on a preprocessed dataset containing program files with malicious and harmless code. The preprocessing of the dataset consists in the fact that program files in the form of machine instructions are translated into a textual description in a formalized language. The model trained in this way is used for the task of classifying software based on the indication of the content of malicious code in it. The article provides information about the conducted experiment on the use of the proposed model. The quality of this approach is evaluated in comparison with existing antivirus technologies. Ways to improve the characteristics of the model are also suggested.

Keywords: antivirus, neural network, language models, malicious code, machine learning, model training, fine tuning, BERT, GPT, GPT-2

The paper analyzes various approaches to identifying and recognizing license plates in intelligent transport networks. A deep learning model has been proposed for localizing and recognizing license plates in natural images, which can achieve satisfactory results in terms of recognition accuracy and speed compared to traditional ones. Evaluations of the effectiveness of the deep learning model are provided.

Keywords: VANET, intelligent transport networks, YOLO, city traffic management system, steganography, deep learning, deep learning, information security, convolutional neural network, CNN

This article discusses modeling of polymer corrosion in aggressive environments based on percolation theory. Within the framework of the work, an algorithm for modeling polymer corrosion was developed, and a program that implements this algorithm in C ++. Paper describes the corrosion modeling algorithm, the structure of the implemented program, and the simulation results for various parameters. The result of this work is an algorithm modeling and an application that performs modeling of the polymer corrosion process in aggressive environments based on the percolation theory according to the developed algorithm, as well as building an image of the damaged material, graphs of the dependence of the length of the boundary of the corrosion region and the area of the remaining material on the modeling step. Algorithm uses Monte-Carlo method for determing area of corrosing region and is suitable for parallel implementation.

Keywords: percolation theory, corrosion modeling, simulation of physical processes, Monte Carlo method, visualization of simulation results