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    Academic Journal

    Contributors: РНФ

    Source: The Herald of the Siberian State University of Telecommunications and Information Science; № 4 (2022); 80-95 ; Вестник СибГУТИ; № 4 (2022); 80-95 ; 1998-6920

    File Description: application/pdf

    Relation: https://vestnik.sibsutis.ru/jour/article/view/549/537; A. Kim, J. Oh, J. Ryu and K. Lee, "A Review of Insider Threat Detection Approaches with IoT Perspective," in IEEE Access, vol. 8, C. 78847-78867, 2020; Kim, J.; Park, M.; Kim, H.; Cho, S.; Kang, P. Insider Threat Detection Based on user Behavior Modeling and Anomaly Detection Algorithms. Appl. Sci. 2019, 9, 4018.; Alpaydin, E. Introduction to Machine Learning; MIT Press: Cambridge, MA, 2014; Al-Mhiqani M. N. et al. A review of insider threat detection: Classification, machine learning techniques, datasets, open challenges, and recommendations //Applied Sciences. – 2020. – Т. 10. – №. 15. – С. 5208.; Al-Mhiqani M. N. et al. A new intelligent multilayer framework for insider threat detection //Computers & Electrical Engineering. – 2022. – Т. 97. – С. 107597.; Rajaguru H., SR S. C. Analysis of decision tree and k-nearest neighbor algorithm in the classification of breast cancer //Asian Pacific journal of cancer prevention: APJCP. – 2019. – Т. 20. – №. 12. – С. 3777.; Sarma M. S. et al. Insider threat detection with face recognition and KNN user classification //2017 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). – IEEE, 2017. – С. 39-44.; Chauhan V. K., Dahiya K., Sharma A. Problem formulations and solvers in linear SVM: a review //Artificial Intelligence Review. – 2019. – Т. 52. – №. 2. – С. 803-855.; Khan S. S., Madden M. G. One-class classification: taxonomy of study and review of techniques //The Knowledge Engineering Review. – 2014. – Т. 29. – №. 3. – С. 345-374.; Buczak A. L., Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection //IEEE Communications surveys & tutorials. – 2015. – Т. 18. – №. 2. – С. 1153-1176.; Le D. C., Zincir-Heywood N. Anomaly detection for insider threats using unsupervised ensembles //IEEE Transactions on Network and Service Management. – 2021. – Т. 18. – №. 2. – С. 1152-1164.; Sadaf K., Sultana J. Intrusion detection based on autoencoder and isolation forest in fog computing //IEEE Access. – 2020. – Т. 8. – С. 167059-167068.; Hariri S., Kind M. C., Brunner R. J. Extended isolation forest //IEEE Transactions on Knowledge and Data Engineering. – 2019. – Т. 33. – №. 4. – С. 1479-1489.; Zhang C., Ma Y. (ed.). Ensemble machine learning: methods and applications. – Springer Science & Business Media, 2012. – С. 1-35.; David, Jisa, and Ciza Thomas. "Efficient DDoS flood attack detection using dynamic thresholding on flow-based network traffic." Computers & Security 82 (2019): 284-295.; Song Y. et al. System level user behavior biometrics using Fisher features and Gaussian mixture models //2013 IEEE Security and Privacy Workshops. – IEEE, 2013. – С. 52-59.; Harilal A. et al. The Wolf Of SUTD (TWOS): A Dataset of Malicious Insider Threat Behavior Based on a Gamified Competition //J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl. – 2018. – Т. 9. – №. 1. – С. 54-85.; Lindauer, Brian (2020): Insider Threat Test Dataset. Carnegie Mellon University. Dataset. https://doi.org/10.1184/R1/12841247.v1; Glasser J., Lindauer B. Bridging the gap: A pragmatic approach to generating insider threat data //2013 IEEE Security and Privacy Workshops. – IEEE, 2013. – С. 98-104.; Al-Shehari T., Alsowail R. A. An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques //Entropy. – 2021. – Т. 23. – №. 10. – С. 1258; Jiang W. et al. An insider threat detection method based on user behavior analysis //International Conference on Intelligent Information Processing. – Springer, Cham, 2018. – С. 421-429.; Bartoszewski F. W. et al. Anomaly Detection for Insider Threats: An Objective Comparison of Machine Learning Models and Ensembles //IFIP International Conference on ICT Systems Security and Privacy Protection. – Springer, Cham, 2021. – С. 367-381.; Aldairi M., Karimi L., Joshi J. A trust aware unsupervised learning approach for insider threat detection //2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI). – IEEE, 2019. – С. 89-98.; Dosh M. Detecting insider threat within institutions using CERT dataset and different ML techniques //Periodicals of Engineering and Natural Sciences. – 2021. – Т. 9. – №. 2. – С. 873-884.; Zou S. et al. Ensemble strategy for insider threat detection from user activity logs //Computers, Materials and Continua. – 2020.; Le D. C., Zincir-Heywood N., Heywood M. I. Analyzing data granularity levels for insider threat detection using machine learning //IEEE Transactions on Network and Service Management. – 2020. – Т. 17. – №. 1. – С. 30-44.; Ferreira P., Le D. C., Zincir-Heywood N. Exploring feature normalization and temporal information for machine learning based insider threat detection //2019 15th International Conference on Network and Service Management (CNSM). – IEEE, 2019. – С. 1-7.; Р. В. Мещеряков, А. Ю. Исхаков, О. О. Евсютин, “Современные методы обеспечения целостности данных в протоколах управления киберфизических систем”, Тр. СПИИРАН, 19:5 (2020), 1089–1122; https://vestnik.sibsutis.ru/jour/article/view/549

  14. 14
    Academic Journal

    Source: A breakthrough in science: development strategies; 188-189 ; Новое слово в науке: стратегии развития; 188-189

    File Description: text/html

    Relation: info:eu-repo/semantics/altIdentifier/isbn/978-5-6048658-0-4; https://interactive-plus.ru/e-articles/830/Action830-557616.pdf; Ганиева Л.Ф. Информационная безопасность в системе открытого образования на примере организации и проведения игры «Международный день Интернета» // Гуманитарные научные исследования. – 2015. – №6(46). – С. 32.; Ганиева Л.Ф. Педагогические, психологические и лингвистические аспекты проблемы киберэкстремизма среди молодежи в вузе / Л.Ф. Ганиева, В.Н. Макашова, А.Ю. Трутнев, И.Н. Новикова // Фундаментальные исследования. – 2016. – №12–6. – С. 1291.; Терещенко Л.К. Правовой режим персональных данных и безопасность личности // Закон. – 2018. – №6. – С. 38.; Трофимова И.А. Обработка и хранение персональных данных // Делопроизводство. – 2015. – №3. – С. 109.; Яковец Е.Н. Своеобразие состава защищаемой конфиденциальной информации // Право и кибербезопасность. – 2014. – №2. – С. 51–53.

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    Academic Journal

    Source: Образование и наука, Vol 0, Iss 4, Pp 169-183 (2017)
    Образование и наука

    File Description: application/pdf

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    Academic Journal

    Source: The Bulletin of Yaroslav Mudryi National Law University. Series:Philosophy, philosophies of law, political science, sociology; Том 1, № 44 (2020); 168-179
    «Вестник НЮУ имени Ярослава Мудрого». Серия: философия, философия права, политология, социология; Том 1, № 44 (2020); 168-179
    "Вісник НЮУ імені Ярослава Мудрого". Серія: Філософія, філософія права, політологія, соціологія; Том 1, № 44 (2020); 168-179

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