Разработка метода имитационного компьютерного моделирования эталонного состояния и поведения SCADA-систем на основе модели акторов
Bibliographic record
Abstract
1. Goldenberg N., Wool A. Accurate modeling of Modbus/TCP for intrusion detection in SCADA systems // International Journal of Critical Infrastructure Protection. 2013. Vol. 6, Issue 2. P. 63–75. 2. Barbosa R. R. R., Sadre R., Pras A. Flow whitelisting in SCADA networks // International Journal of Critical Infrastructure Protection. 2013. Vol. 6, Issue 3–4. P. 150–158. 3. Weaver P. SNORT IDS for SCADA Systems / RedHat 5 Enterprise Installation Guide Featuring SCADA ICCP Signatures. URL: http://www.snort.org/assets/114/Snort_RH5_SCADA. pdf (дата обращения: 30.12.2013). 4. Zhu B., Sastry S. SCADA-specific Intrusion Detection / Prevention Systems: A Survey and Taxonomy. URL: http://www.cse.psu.edu/~smclaugh/cse598e-f11/papers/zhu.pdf (дата обращения: 30.12.2013). 5. Byres J. Honeywell selects Tofino™ Modbus Read-only Firewall to Secure Critical Safety Systems. The Univ. of British Columbia. Canada, 2011. URL: http://www.tofinosecurity.com/sites/default/files/pr_hon_modbus_read-only_firewall_01_06_11.pdf (дата обращения: 30.12.2013). 6. Zeng W. Secure Distributed Control Methodologies with Built-in Defense in Distributed Networked Control Systems / North Carolina State University, Raleigh, North Carolina, USA. 2013. URL: http://www.lib.ncsu.edu/resolver/1840.16/8911 (дата обращения: 30.12.2013). 7. Васенин В. А. Критическая энергетическая инфраструктура: кибертеррористическая угроза // Информационные технологии. 2009. № 9. С. 2–8. 8. Byres E., Howard S. Analysis of the Siemens WinCC / PCS7 «Stuxnet» Malware for Industrial Control System Professionals / Tofino Security. Lantzville, BC, Canada. 2010. URL: http://www.scadahacker.com/library/Documents/ICS_Events/Analysis%20of%20Siemens%20Malware%20Attacks%20v3.1%20(Tofino%20Security).pdf (дата обращения: 30.12.2013). 9. Кубышкин А. С. Разработка модели разграничения прав доступа для автоматизированных систем технологического управления // Вестн. Новосиб. гос. ун-та. Серия: Информационные технологии. 2012. Т. 10, вып. 3. С. 26–33. 10. Meixell B., Forner E. Out of Control: Demonstrating SCADA Exploitation // Black Hat 2013. Black Hat Conference. Las Vegas, Nevada, USA, 2013. URL: https://media.blackhat.com/us13/US-13-Forner-Out-of-Control-Demonstrating-SCADA-Slides.pdf (дата обращения: 30.12.2013). 11. Stouffer K., Falco J., Scarfone K. Guide to Industrial Control Systems (ICS) Security // National Institute of Standards and Technology Gaithersburg. Gaithersburg, Maryland, USA, 2011. URL: http://csrc.nist.gov/publications/nistpubs/800-82/SP800-82-final.pdf (дата обращения: 30.12.2013). 12. Byres E. Patching for Control Systems – A Broken Model? / Tofino Security. Lantzville, BC, Canada, 2013. URL: https://www.tofinosecurity.com/downloads/691 (дата обращения:30.12.2013). 13. Seymour B., Kabay E. IS Auditing Procedure – Security Assessment – Penetration Testing and Vulnerability Analyses. Document P8 / ISACA. ISACA (Information System Audit and Control Association), Rolling Meadows, Illinois, USA. 2004. URL: http://trygstad.rice.iit.edu: 8000/Audits/Audit%20Checklists/ISAuditingP8PenetrationTesting-ISACA.pdf (дата обращения:30.12.2013). 14. Gritsai G., Timorin A., Goltsev Y., Ilin R., Gordeychik S., Karpin A. SCADA Safety In Numbers V1.1* / Positive Technologies. Moscow, Russian Federation, 2012. URL: http://www.ptsecurity.com/download/SCADA_analytics_english.pdf (дата обращения: 30.12.2013). 15. Ревнивых А. В., Федотов А. М. Обзор политик информационной безопасности // Вестн. Новосиб. гос. ун-та. Серия: Информационные технологии. 2012. Т. 10, вып. 3. C. 66–79. 16. Хемди А. Таха. Имитационное моделирование // Введение в исследование операций. Operations Research: An Introduction. 7-е изд. М.: Вильямс, 2007. С. 667–705. 17. Михеева Т. В. Информационные технологии имитационного моделирования в организации корпоративной производственной системы // Вестн. Новосиб. гос. ун-та. Серия: Информационные технологии. 2009. Т. 7, вып. 2. С. 42–48. 18. Agha G. Actors: A Model of Concurrent Computation in Distributed Systems. Cambridge: MIT Press Series in Artificial Intelligence, 1986. 19. Xiaojun Liu, Jie Liu, Eker J., Lee E. A. Heterogeneous Modeling and Design of Control Systems / Department of Electrical Engineering and Computer Sciences University of California. Berkeley, California, USA. 2001. URL: http://sec.eecs.berkeley.edu/papers/01/controlsys/controlsys.pdf (дата обращения: 10.03.2014). 20. Витяев Е. Е., Ковалерчук Б. Я., Федотов А. М., Барахнин В. Б., Дурдин Д. С., Белов С. Д., Демин А. В. Обнаружение закономерностей и распознавание аномальных событий в потоке данных сетевого трафика // Вестн. Новосиб. гос. ун-та. Серия: Информационные технологии. 2008. Т. 6, вып. 2. С. 57–68. 21. Безукладников И. И., Кон Е. Л. Проблема скрытых каналов в промышленных информационно-управляющих и инфокоммуникационных сетях // Промышленные АСУ и контроллеры. 2011. № 7. С. 61–64.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".