ellibsЭлектронные библиотекиRussian Digital Libraries Journal1562-5419Казанский (Приволжский) федеральный университет10.26907/1562-5419-2024-27-4-679-694ellibs-566Research ArticleСтатьиПрименение алгоритма Дугласа–Пеккера в вопросах онлайн-аутентификации инструментов удалённой работы при подготовке специалистов укрупнённой группы специальностей 10.00.00 «Информационная безопасность»Application of the Douglas-Peucker Algorithm in Online Authentication of Remote Work Tools for Specialist Training in Higher Education Group of Scientific Specialties (UGSN) 10.00.00УйминАнтон ГригорьевичUyminAnton Grigorievichau-mail@ya.ruГрековВладимир СергеевичGrekovVladimir Sergeyevichgrekov.vs.work@gmail.comРоссийский государственный университет нефти и газа (Национальный исследовательский университет) имени И.М. ГубкинаGubkin Russian State University of Oil and Gas (National Research University)202428052025274679694Copyright © Уймин А.Г., Греков В.С., 20252025Уймин А.Г., Греков В.С.Uymin A.G., Grekov V.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ellibs.elpub.ru/jour/article/view/566

В условиях перехода образовательных систем на дистанционное обучение, а также развития тренда на удалённую работу, возникла острая потребность в разработке надежных технологий биометрической идентификации и аутентификации для верификации исполнителей работ в режиме удаленной работы. Такие технологии позволяют обеспечить высокую степень защиты и удобство использования, что делает вопросы их разработки и оптимизации крайне важными. Проблема заключается в необходимости повышения точности и эффективности систем распознавания движений манипулятора «мышь» без использования специализированных устройств в максимально короткий промежуток времени. Для ее решения требуется эффективная предобработка таких движений, чтобы упростить их траектории, сохранив при этом их ключевые особенности. В статье предложено использование алгоритма Дугласа–Пеккера для предварительной обработки данных траекторий движений «мыши». Этот алгоритм позволяет значительно уменьшить количество точек в траекториях, упрощая их при сохранении основной формы движений. Данные с упрощенными траекториями затем используются для обучения нейронных сетей. Экспериментальная часть работы показала, что применение алгоритма Дугласа–Пеккера позволяет сократить количество точек в траекториях на 60%, что приводит к увеличению точности распознавания движений с 70% до 82%. Такое упрощение данных способствует ускорению процесса обучения нейронных сетей и повышению их операционной эффективности. Проведенное исследование подтвердило эффективность использования алгоритма Дугласа–Пеккера для предварительной обработки данных в задачах распознавания движений «мыши». Полученные результаты могут найти применение в разработке более интуитивно понятных и адаптивных пользовательских интерфейсов. Предложены также направления для дальнейших исследований, включая оптимизацию параметров алгоритма для различных типов движений и исследование возможности его комбинирования с другими методами машинного обучения.

In today's world, digital technologies are penetrating all aspects of human activity, including education and labor. Since 2019, when, in response to global challenges, the world's educational systems have actively started to shift to distance learning, there has been an urgent need to develop and implement reliable identification and authentication technologies. These technologies are necessary to ensure the authenticity of work and protection from falsification of academic achievements, especially in the context of higher education in accordance with the group of specialties and directions (USGS) 10.00.00 - Information Security, where laboratory and practical work play a key role in the educational process. The problem lies in the need to optimize the flow of incoming data, which, first, can affect the retraining of the neural network core of the recognition system, and second, impose excessive requirements on the network's bandwidth. To solve this problem, efficient preprocessing of gesture data is required to simplify their trajectories while preserving the key features of the gestures. This article proposes the use of the Douglas–Peucker algorithm for preliminary processing of mouse gesture trajectory data. This algorithm significantly reduces the number of points in the trajectories, simplifying them while preserving the main shape of the gestures. The data with simplified trajectories are then used to train neural networks. The experimental part of the work showed that the application of the Douglas–Peucker algorithm allows for a 60% reduction in the number of points in the trajectories, leading to an increase in gesture recognition accuracy from 70% to 82%. Such data simplification contributes to speeding up the neural networks' training process and improving their operational efficiency. The study confirmed the effectiveness of using the Douglas–Peucker algorithm for preliminary data processing in mouse gesture recognition tasks. The article suggests directions for further research, including the optimization of the algorithm's parameters for different types of gestures and exploring the possibility of combining it with other machine learning methods. The obtained results can be applied to developing more intuitive and adaptive user interfaces.

аутентификациябиометрическая идентификацияудалённая работадистанционное обучениеалгоритм Дугласа–Пеккерапредобработка данныхнейросетьHID-устройствотраектория движений «мыши»оптимизация данныхauthenticationbiometric identificationremote workdistance learningDouglas–Peucker algorithmdata preprocessingneural networkHID devicesmouse gesture trajectoriesdata optimizationReferencesYusuf N. et al. A survey of biometric approaches of authentication // International Journal of Advanced Computer Research. 2020. Vol. 10. No. 47. P. 96–104.Yusuf N. et al. A survey of biometric approaches of authentication // International Journal of Advanced Computer Research. 2020. Vol. 10. No. 47. P. 96–104.Sarkar A., Singh B.K. A review on performance, security and various biometric template protection schemes for biometric authentication systems // Multimedia Tools and Applications. 2020. Vol. 79. No. 37. P. 27721–27776.Sarkar A., Singh B.K. A review on performance, security and various biometric template protection schemes for biometric authentication systems // Multimedia Tools and Applications. 2020. Vol. 79. No. 37. P. 27721–27776.Alwahaishi S., Zdrálek J. Biometric authentication security: an overview // 2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2020. P. 87–91.Alwahaishi S., Zdrálek J. Biometric authentication security: an overview // 2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2020. P. 87–91.López C. et al. Adversarial attacks against mouse-and keyboard-based biometric authentication: black-box versus domain-specific techniques //International Journal of Information Security. 2023. Vol. 22. No. 6. P. 1665–1685.López C. et al. Adversarial attacks against mouse-and keyboard-based biometric authentication: black-box versus domain-specific techniques //International Journal of Information Security. 2023. Vol. 22. No. 6. P. 1665–1685.Thomas P.A., Preetha Mathew K. A broad review on non-intrusive active user authentication in biometrics // Journal of Ambient Intelligence and Humanized Computing. 2023. Vol. 14. No. 1. P. 339–360.Thomas P.A., Preetha Mathew K. A broad review on non-intrusive active user authentication in biometrics // Journal of Ambient Intelligence and Humanized Computing. 2023. Vol. 14. No. 1. P. 339–360.Dahia G., Jesus L., Pamplona Segundo M. Continuous authentication using biometrics: An advanced review // Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2020. Vol. 10. No. 4. P. e1365.Dahia G., Jesus L., Pamplona Segundo M. Continuous authentication using biometrics: An advanced review // Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2020. Vol. 10. No. 4. P. e1365.Ryu R. et al. Continuous multimodal biometric authentication schemes: a systematic review // IEEE Access. 2021. Vol. 9. P. 34541–34557.Ryu R. et al. Continuous multimodal biometric authentication schemes: a systematic review // IEEE Access. 2021. Vol. 9. P. 34541–34557.Almalki S., Assery N., Roy K. An empirical evaluation of online continuous authentication and anomaly detection using mouse clickstream data analysis // Applied Sciences. 2021. Vol. 11. No. 13. P. 6083.Almalki S., Assery N., Roy K. An empirical evaluation of online continuous authentication and anomaly detection using mouse clickstream data analysis // Applied Sciences. 2021. Vol. 11. No. 13. P. 6083.Almalki S., Chatterjee P., Roy K. Continuous authentication using mouse clickstream data analysis // Security, Privacy, and Anonymity in Computation, Communication, and Storage: SpaCCS 2019 International Workshops, Atlanta, GA, USA, July 14–17, 2019, Proceedings 12. Springer International Publishing, 2019. P. 76–85.Almalki S., Chatterjee P., Roy K. Continuous authentication using mouse clickstream data analysis // Security, Privacy, and Anonymity in Computation, Communication, and Storage: SpaCCS 2019 International Workshops, Atlanta, GA, USA, July 14–17, 2019, Proceedings 12. Springer International Publishing, 2019. P. 76–85.Jorgensen Z., Yu T. On mouse dynamics as a behavioral biometric for authentication // Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security. 2011. P. 476–482.Jorgensen Z., Yu T. On mouse dynamics as a behavioral biometric for authentication // Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security. 2011. P. 476–482.Sayed B. et al. Biometric authentication using mouse gesture dynamics // IEEE systems journal. 2013. Vol. 7. No. 2. P. 262–274.Sayed B. et al. Biometric authentication using mouse gesture dynamics // IEEE systems journal. 2013. Vol. 7. No. 2. P. 262–274.Siddiqui N. et al. Machine and deep learning applications to mouse dynamics for continuous user authentication // Machine Learning and Knowledge Extraction. 2022. Vol. 4. No. 2. P. 502–518.Siddiqui N. et al. Machine and deep learning applications to mouse dynamics for continuous user authentication // Machine Learning and Knowledge Extraction. 2022. Vol. 4. No. 2. P. 502–518.Zhang Y.G. et al. Trustworthy interaction model: continuous authentication using time–frequency joint analysis of mouse biometrics // Behaviour & Information Technology. 2024. P. 1–18.Zhang Y.G. et al. Trustworthy interaction model: continuous authentication using time–frequency joint analysis of mouse biometrics // Behaviour & Information Technology. 2024. P. 1–18.Fenu G., Marras M., Boratto L. A multi-biometric system for continuous student authentication in e-learning platforms // Pattern Recognition Letters. 2018. Vol. 113. P. 83–92.Fenu G., Marras M., Boratto L. A multi-biometric system for continuous student authentication in e-learning platforms // Pattern Recognition Letters. 2018. Vol. 113. P. 83–92.Gao Q. Biometric authentication to prevent e-cheating // International Journal of Instructional Technology and Distance Learning. 2012. Vol. 9. No. 2. P. 3–14.Gao Q. Biometric authentication to prevent e-cheating // International Journal of Instructional Technology and Distance Learning. 2012. Vol. 9. No. 2. P. 3–14.Hernandez-Ortega J. et al. edBB: Biometrics and behavior for assessing remote education // arXiv preprint arXiv:1912.04786. 2019.Hernandez-Ortega J. et al. edBB: Biometrics and behavior for assessing remote education // arXiv preprint arXiv:1912.04786. 2019.Abuzaraida M.A., Jebriel S.M. The detection of the suitable reduction value of Douglas–Peucker algorithm in online handwritten recognition systems // 2015 IEEE International Conference on Service Operations and Logistics, And Informatics (SOLI). IEEE, 2015. P. 82–87.Abuzaraida M.A., Jebriel S.M. The detection of the suitable reduction value of Douglas–Peucker algorithm in online handwritten recognition systems // 2015 IEEE International Conference on Service Operations and Logistics, And Informatics (SOLI). IEEE, 2015. P. 82–87.Fong T. et al. PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment // eNeuro. 2023. Vol. 10. No. 5.Fong T. et al. PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment // eNeuro. 2023. Vol. 10. No. 5.Uymin A. Instruments for student verification and assessment of his emotional and psychological state during remote work // Norwegian Journal of development of the International Science. 2022. No. 96. P. 98–101.Uymin A. Instruments for student verification and assessment of his emotional and psychological state during remote work // Norwegian Journal of development of the International Science. 2022. No. 96. P. 98–101.Uymin A. Preprocessing data from the mouse manipulator for use in behavioral biometrics analysis // Norwegian Journal of Development of the International Science. 2022. No. 85. P. 53–58.Uymin A. Preprocessing data from the mouse manipulator for use in behavioral biometrics analysis // Norwegian Journal of Development of the International Science. 2022. No. 85. P. 53–58.

The authors declare that there are no conflicts of interest present.