Персона: Зайцев, Константин Сергеевич
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Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
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- ПубликацияТолько метаданныеStudying open banking platforms with open source code(2019) Kolychev, A.; Zaytsev, K.; Зайцев, Константин Сергеевич© 2005–ongoing JATIT and LLS. All rights reserved.Intensive growth of public web interfaces started early in 2010; and if initially API was a procedure of interaction of various software tools, then at present web interfaces are genuine digital products on the basis of which companies, especially major companies, can derive profits while providing their internal services to third parties via open API. Banks are not an exception. They also can derive profits by providing access to their internal services for third-party developers. The advantage of banking enterprises is that they possess unique data and services, which can hardly be competed. As a consequence, there appeared the software market for the development of open source API and provision of access to them with monetization capabilities. API management platform is comprised generally of three components: developer site, API development tools, and API gateway. API gateway is the most important component since it is responsible for interface operation; hence, this work is aimed at the determination of the most efficient API gateways. Three software variants have been considered: Gravitee API Platform, APIMan, and WSO2 API Manager, which meet two preset criteria: Java product implementation, open source code of the product. The study has been performed in comparison environment with three coordinates: intensity of performed functions for API development, labor intensity of API implementation, the performance of API gateway. During the experiments, Gravitee.Io API Platform was the best software with regard to each coordinate.
- ПубликацияТолько метаданныеLogs analysis to search for anomalies in the functioning of large technology platforms(2019) Dunaev, M.; Zaytsev, K.; Дунаев, Максим Евгеньевич; Зайцев, Константин Сергеевич© 2005–ongoing JATIT and LLS. All rights reserved.Today, with the widespread use of machine learning methods in various fields of human activity, the detection of rare events still remains one the most challenging tasks. This is due to the fact that there is very little information to learn computers to detect deviations from normal operation, although it has to deal with the processing of very large amounts of data that characterize the ongoing processes. This occurs, for example, in high energy physics, when searching for and studying new particles. The similar situation occurs when detecting pre-anomalous situations in the complex high-tech equipment operation. Logs are the only source of information to detect the processes running on such equipment, therefore many IT companies use them to analyze the functioning of their software and hardware technologies. This allows viewing the logs starting from very beginning to the point of failure completion, consistently figuring out the possible causes of the incident. In most companies, this process is not automated, because there is no single established approach to analyze logs of different configurations of stored metric values and different filling intensities. In addition, historical logs are not used to predict the sequence of events that lead to anomalies in the operation of any software technologies. The present article deals with the problem of detecting states and predicting the nearest behavior of large technological platforms by directional analysis of their logs. Usually, logs of large technology platforms represent data sets of very high dimensionality that does not allow modern algorithms in the allowable time limits to draw the necessary conclusions about the behavior of platforms and form sequence of control actions, if necessary. To solve this problem, the article compares the effectiveness of existing algorithms, traditionally used unsupervised learning, because the available data for learning are too small, as well as algorithms working with big data. Pilot implementations of all algorithms involved in solving the problem, performed in Python programming language, have been studied in a single environment. Based on their comparison, the most efficient algorithm was chosen, when recognizing different types of events based on real data. The solution of the chosen algorithm was implemented using Apache Spark framework. Additional investigation has shown that the selected algorithm can work in real time mode.
- ПубликацияТолько метаданныеStudying systems of open source messaging(2019) Bondarenko, A.; Zaytsev, K.; Зайцев, Константин Сергеевич© 2005 - ongoing JATIT & LLSModern large industrial and financial structures apply numerous various information systems (IS) which exchange data while communicating with each other. In order to implement such communication nowadays, specialized messaging systems are used or transport components comprised of one or several software products. This article compares four open source software products used in messaging systems: Apache Kafka, gRPC, ZeroMQ, and RabbitMQ, which satisfy criteria of Secure Sockets Layer/Transport Layer Security (SSL/TLS) encryption and possibility to operate directly with Java platform applications, that is, to provide Java API. In order to perform these studies, comparison environment was generated with four coordinates: supported communication type, productivity, reliability, and community support.
- ПубликацияТолько метаданныеGENERATION OF MATHEMATICAL MODELS OF LINEAR DYNAMIC SYSTEMS DESCRIBED BY BLOCK DIAGRAMS(2022) Simonov, M.; Zaytsev, K.; Popova, N.; Зайцев, Константин Сергеевич
- ПубликацияТолько метаданныеANOMALY DETECTION IN STREAM DATA PROCESSING IN REAL TIME(2022) Dunaev, M.; Zaytsev, K.; Elchenkov, R.; Savitsky, D.; Дунаев, Максим Евгеньевич; Зайцев, Константин Сергеевич© 2022 Little Lion ScientificThe purpose of the present work is to study methods for solving problems of anomaly detection and prediction of time series values when processing streaming data in real-time in a network environment and their improvement. To solve this problem the authors propose a Real-Time K-Means modification with preliminary markup. The effectiveness of the modification is confirmed by comparing it with the frequently used Streaming K-Means from the Apache Spark Mllib library. To solve the problem of predicting time series when processing streaming data in real-time, the authors propose a modification of the autoregression model with a given AR order by adding the inheritance function of the previous values of the time series to it. The results of comparative experiments of the proposed Real-Time AR modification with classical AR confirmed the effectiveness of the modification, which is especially evident in the presence of anomalies in the behavior of the time series. The proposed algorithm modifications allow not only parallelizing calculations using the deferred computing paradigm but also configuring the model fleetingly in the Apache Spark ecosystem. To conduct experiments with algorithms, a dataset was built – a data slice from 1,000 measurements of the Apache Kafka server metrics log with one topic, two producers, and one consumer. Anomalous fragments were artificially added to the dataset, differing by a large number of messages per second and/or message size. The values of the original dataset were normalized and shifted to the average value of the training fetch. Moreover, static and highly correlated metrics were eliminated. The results of the application of the developed algorithms in solving the problems of detecting and predicting the values of time series have shown that even the presence of behavior anomalies does not distort predictions significantly.
- ПубликацияТолько метаданныеDEVELOPMENT OF NEURAL NETWORK MODELS FOR OBTAINING INFORMATION ABOUT NODULAR NEOPLASMS OF THE THYROID GLAND BASED ON ULTRASOUND IMAGES(2023) Lozhkin, I.; Tcyguleva, K.; Zaytsev, K.; Dunaev, M.; Garmash, A.; Ложкин, Илья Александрович; Цыгулева, Ксения Владимировна; Зайцев, Константин Сергеевич; Дунаев, Максим Евгеньевич; Гармаш, Александр Александрович
- ПубликацияТолько метаданныеDETECTION OF GEOMAGNETIC STORM SUDDEN COMMENCEMENTS WITH THE USE OF NEURAL NETWORK ARCHITECTURES(2024) Voloshin, T.; Zaytsev, K.; Волошин, Тарас Андреевич; Зайцев, Константин Сергеевич