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1Academic Journal
Συγγραφείς: K. A. Gaiduk, A. Y. Iskhakov, К. А. Гайдук, А. Ю. Исхаков
Συνεισφορές: РНФ
Πηγή: The Herald of the Siberian State University of Telecommunications and Information Science; № 4 (2022); 80-95 ; Вестник СибГУТИ; № 4 (2022); 80-95 ; 1998-6920
Θεματικοί όροι: внутренние угрозы информационной безопасности, машинное обучение, поиск аномалий, аутентификация, изоляционный лес, ансамблевые методы
Περιγραφή αρχείου: 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
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2Academic Journal
Θεματικοί όροι: 5. Gender equality, управление информационной безопасностью, 13. Climate action, 9. Industry and infrastructure, information security management, insider, инсайдер, internal information security threats, 16. Peace & justice, внутренние угрозы информационной безопасности
Σύνδεσμος πρόσβασης: https://research-journal.org/wp-content/uploads/2019/05/5-1-83.pdf#page=26
https://cyberleninka.ru/article/n/o-vnutrennih-ugrozah-informatsionnoy-bezopasnosti
https://research-journal.org/technical/o-vnutrennix-ugrozax-informacionnoj-bezopasnosti/ -
3Academic Journal
Θεματικοί όροι: управление информационной безопасностью, 9. Industry and infrastructure, information security management, 1. No poverty, 16. Peace & justice, внутренние угрозы информационной безопасности, 12. Responsible consumption, 5. Gender equality, 8. Economic growth, insider, инсайдер, maturity model, internal threats to information security, модель зрелости, CMMI
Σύνδεσμος πρόσβασης: https://research-journal.org/wp-content/uploads/2019/04/4-1-82.pdf#page=57
https://cyberleninka.ru/article/n/primenenie-modeli-zrelosti-dlya-protivodeystviya-insayderskim-ugrozam-informatsionnoy-bezopasnosti
https://research-journal.org/en/engineering/primenenie-modeli-zrelosti-dlya-protivodejstviya-insajderskim-ugrozam-informacionnoj-bezopasnosti/ -
4Academic Journal
Συγγραφείς: Маркова, Татьяна, Захарова, Ксения
Θεματικοί όροι: ИНСАЙДЕРЫ, ВНУТРЕННИЕ УГРОЗЫ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ,
ГРУППА ЛОЯЛЬНЫХ ИНСАЙДЕРОВ: "ХАЛАТНЫЕ" И "МАНИПУЛИРУЕМЫЕ", ГРУППА ЗЛОНАМЕРЕННЫХ ИНСАЙДЕРОВ: "ОБИЖЕННЫЙ", "НЕЛОЯЛЬНЫЙ", "ПОДРАБАТЫВАЮЩИЙ" И "ВНЕДРЕННЫЙ"., A GROUP OF LOYAL INSIDERS: "CARELESS" AND "MANIPULATED", A GROUP OF MALICIOUS INSIDERS: "HURT", "DISLOYAL", "MOONLIGHTING" AND "EMBEDDED" Περιγραφή αρχείου: text/html
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5Academic Journal
Πηγή: Вестник Волжского университета им. В.Н. Татищева.
Περιγραφή αρχείου: text/html
