At the moment, quantum key distribution (QKD) technology guarantees the highest level of data exchange security, which makes QKD networks one of the most promising areas in the field of computer security. Unfortunately, the problem of topology optimization when planning and extending QKD networks has not attracted enough attention. This paper reviews approaches that use analytical models in the topology optimization problem of quantum key distribution networks. Different methods that solve problems of network capacity and security maximization and cost minimization are reviewed, the utilized algorithms are described, and conclusions about possible further research in this area are drawn.

Keywords: quantum key distribution, mathematical modeling, network topology, analytical modeling, topology optimization

The paper is devoted to the development of a security concept for the protection of critical information infrastructure of the financial sector. The critical information infrastructure of the financial sector is analyzed, and the main types of cyberattacks are considered in relation to the objects in this area. The concept of security is proposed, including access control, multilevel protection, data encryption, continuous monitoring and other measures. Models of the main threats to the security of information infrastructure objects of the financial sector are given. The question of the importance of cooperation and information exchange between financial institutions, regulators and law enforcement agencies to ensure collective security of the financial sector is raised. The article will be useful for specialists in the field of information security, financial sector and managers of organizations interested in developing and improving the security system of information infrastructure of the enterprise.

Keywords: information security, information infrastructure, financial sector, mathematical modeling, software package

This paper describes the development of a hybrid system for recognition of various types of personal data in unstructured texts in Russian language. The system is based on neural network and regular expressions. Regular expressions were used to detect structured entities such as telephone and passport numbers. In order to detect named entities, including persons, locations and organizations, the neural network was used. For training and validation, a specialized Russian-language dataset for named entity recognition was created based on Nerus and WiNER labeled datasets. The proposed neural model is using contextualized ELMo embeddings and includes bidirectional LSTM layers with conditional random field layer (ELMo-BiLSTM-CRF). The performance of the resulting model was analyzed on the validation set, including accuracy on individual classes. During the evaluation, 4 different metrics were used, including precision, recall, f1-score and macro-f1. For more detailed analysis, a confusion matrix was created. The resulting hybrid model can be utilized to reduce the cost of storing and processing textual data, as well as preserve user privacy in case of leaks.

Keywords: personal data, natural language processing, named entity recognition, conditional random field, neural network, recurrent neural network, regular expression