TY - JOUR A1 - Gavrylenko, Svitlana A1 - Poltoratskyi, Vadym A1 - Nechyporenko, Alina T1 - Intrusion Detection Model Based on Improved Transformer JF - Advanced Information Systems N2 - The object of the study is the process of identifying the state of a computer network. The subject of the study are the methods of identifying the state of computer networks. The purpose of the paper is to improve the efficacy of intrusion detection in computer networks by developing a method based on transformer models. The results obtained. The work analyzes traditional machine learning algorithms, deep learning methods and considers the advantages of using transformer models. A method for detecting intrusions in computer networks is proposed. This method differs from known approaches by utilizing the Vision Transformer for Small-size Datasets (ViTSD) deep learning algorithm. The method incorporates procedures to reduce the correlation of input data and transform data into a specific format required for model operations. The developed methods are implemented using Python and the GOOGLE COLAB cloud service with Jupyter Notebook. Conclusions. Experiments confirmed the efficiency of the proposed method. The use of the developed method based on the ViTSD algorithm and the data preprocessing procedure increases the model's accuracy to 98.7%. This makes it possible to recommend it for practical use, in order to improve the accuracy of identifying the state of a computer system. KW - data preprocessing KW - machine learning KW - neural network KW - RNN KW - SVM KW - KNN KW - transformer model KW - vision transformer Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18750 SN - 2522-9052 VL - 8 IS - 1 SP - 94 EP - 99 PB - National Technical University Kharkiv Polytechnic Institute ER -