Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos
Las aplicaciones emergentes de automatización industrial demandan gran flexibilidad en los sistemas, lo cual se logra con en el aumento de la interconexión entre sus módulos, permitiendo el acceso a toda la información del sistema y la reconfiguración en función de los cambios que se presentan duran...
- Autores:
-
Paredes Valencia, Carlos Mario
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2022
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- spa
- OAI Identifier:
- oai:red.uao.edu.co:10614/14003
- Acceso en línea:
- https://hdl.handle.net/10614/14003
https://red.uao.edu.co/
- Palabra clave:
- Doctorado en Ingeniería
Ciberinteligencia (Seguridad informática)
Ciberterrorismo
Computación ubicua
Cyber intelligence (Computer security)
Cyberterrorism
Ubiquitous computing
Ciberataques
Sistema ciberfísico
Sistema de detección de ciberataques
Cyber-attacks
Cyber-physical system
Cyber-attack detection system
- Rights
- openAccess
- License
- Derechos reservados - Universidad Autónoma de Occidente, 2022
| id |
REPOUAO2_da7bb20974552d2cb6fa1b12886d0733 |
|---|---|
| oai_identifier_str |
oai:red.uao.edu.co:10614/14003 |
| network_acronym_str |
REPOUAO2 |
| network_name_str |
RED: Repositorio Educativo Digital UAO |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos |
| title |
Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos |
| spellingShingle |
Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos Doctorado en Ingeniería Ciberinteligencia (Seguridad informática) Ciberterrorismo Computación ubicua Cyber intelligence (Computer security) Cyberterrorism Ubiquitous computing Ciberataques Sistema ciberfísico Sistema de detección de ciberataques Cyber-attacks Cyber-physical system Cyber-attack detection system |
| title_short |
Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos |
| title_full |
Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos |
| title_fullStr |
Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos |
| title_full_unstemmed |
Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos |
| title_sort |
Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos |
| dc.creator.fl_str_mv |
Paredes Valencia, Carlos Mario |
| dc.contributor.advisor.none.fl_str_mv |
Martínez Castro, Diego |
| dc.contributor.author.none.fl_str_mv |
Paredes Valencia, Carlos Mario |
| dc.subject.spa.fl_str_mv |
Doctorado en Ingeniería |
| topic |
Doctorado en Ingeniería Ciberinteligencia (Seguridad informática) Ciberterrorismo Computación ubicua Cyber intelligence (Computer security) Cyberterrorism Ubiquitous computing Ciberataques Sistema ciberfísico Sistema de detección de ciberataques Cyber-attacks Cyber-physical system Cyber-attack detection system |
| dc.subject.armarc.spa.fl_str_mv |
Ciberinteligencia (Seguridad informática) Ciberterrorismo Computación ubicua |
| dc.subject.armarc.eng.fl_str_mv |
Cyber intelligence (Computer security) Cyberterrorism Ubiquitous computing |
| dc.subject.proposal.spa.fl_str_mv |
Ciberataques Sistema ciberfísico Sistema de detección de ciberataques |
| dc.subject.proposal.eng.fl_str_mv |
Cyber-attacks Cyber-physical system Cyber-attack detection system |
| description |
Las aplicaciones emergentes de automatización industrial demandan gran flexibilidad en los sistemas, lo cual se logra con en el aumento de la interconexión entre sus módulos, permitiendo el acceso a toda la información del sistema y la reconfiguración en función de los cambios que se presentan durante su funcionamiento, con el propósito de alcanzar puntos óptimos de operación. Para ello se soportan en el concepto de sistemas ciberfísicos (CPSs, Cyber-physical Systems por sus siglas en inglés), los cuales se caracterizan por la integración de sistemas físicos y digitales para crear productos y procesos inteligentes capaces de transformar las cadenas de valor convencionales, lo que ha dado origen al concepto de Smart Factory. Esta flexibilidad abre una gran brecha que afecta a la seguridad de los sistemas de control ya que los nuevos enlaces de comunicación pueden ser utilizados por personas para generar ataques que produzcan riesgo en estas aplicaciones. Este es un problema reciente en los sistemas de control, que originalmente estaban centralizados y posteriormente se implementaron como sistemas interconectados a través de redes aisladas. Actualmente, para proteger estos sistemas se ha optado por utilizar estrategias que han presentado resultados destacables en otros ambientes, como por ejemplo los ambientes de oficina. Sin embargo, las características de estas aplicaciones no son las mismas y los resultados alcanzados no son los deseados. Esta problemática ha motivado varios esfuerzos que pretenden contribuir desde diferentes enfoques a aumentar la seguridad de los sistemas de control. Se realizó una revisión de las estrategias utilizadas actualmente para el diseño de redes de control seguras, y de las técnicas y tecnologías que buscan detectar ataques en los sistemas de control y contribuir a mitigar el efecto de los mismos. Esta revisión permitió identificar los ataques que tienen mayor frecuencia e impacto en estos sistemas, a partir de lo cual se seleccionaron los ataques que comprometen la integridad de las variables de los sistemas de control y los retrasos en el envío de los mensajes que contienen estos valores, para ser abordados en esta propuesta. Con base en lo anterior, en este trabajo se propuso un procedimiento de diseño de aplicaciones de control soportadas en sistemas ciberfísicos que posibilita la implementación de estrategias de detección y tolerancia de ciberataques, el cual integra un enfoque modular y de fácil adaptación. Este procedimiento permite identificar los diversos componentes del sistema los cuales se establecen como microservicios, e integra un planteamiento para evaluar la planificabilidad de los componentes de la aplicación de control. Las etapas del procedimiento detallan el desarrollo de sistemas de detección y aislamiento de ciberataques que son usados para generar alarmas, a partir de las cuales es posible definir qué elemento del sistema está siendo afectado, posibilitando el uso de estrategias soportadas en réplicas de componentes que permitan el reemplazo de los mismos para tolerar ciberataques. La arquitectura y el procedimiento propuesto permiten el cumplimiento de los requisitos del sistema, y presentan un enfoque modular y de fácil adaptabilidad para el diseño. |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-06-22T17:24:46Z |
| dc.date.available.none.fl_str_mv |
2022-06-22T17:24:46Z |
| dc.date.issued.none.fl_str_mv |
2022-04 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_71e4c1898caa6e32 |
| dc.type.coar.eng.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.eng.fl_str_mv |
Text |
| dc.type.driver.eng.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| format |
http://purl.org/coar/resource_type/c_db06 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10614/14003 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Autónoma de Occidente |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Educativo Digital |
| dc.identifier.repourl.spa.fl_str_mv |
https://red.uao.edu.co/ |
| url |
https://hdl.handle.net/10614/14003 https://red.uao.edu.co/ |
| identifier_str_mv |
Universidad Autónoma de Occidente Repositorio Educativo Digital |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.cites.spa.fl_str_mv |
Paredes Valencia, C. M. (2022). Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos. Cali. (Tesis). Universidad Autónoma de Occidente. Cali. Colombia |
| dc.relation.references.none.fl_str_mv |
[1] A. Humayed, J. Lin, F. Li, y B. Luo, “Cyber-physical systems security - a survey,” IEEE Internet Things J, vol. 4, no. 6, pp. 1802–1831,. [2] A. Cardenas, S. Amin, Z.-S. Lin, Y. Huang, C.-Y. Huang, y S. Sastry, “Attacks against process control systems: Risk assessment, detection, and response,” in Proceedings of the 6th International Symposium on Information, Computer and Communications Security, ASIACCS 2011, p. 355–366. [3] Y. Shoukry, “Smt-based observer design for cyber-physical systems under sensor attacks,” in 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems, ICCPS 2016 - Proceedings. [4] H. Fawzi, P. Tabuada, y S. Diggavi, “Security for control systems under sensor and actuator attacks,” in 2012 IEEE 51st IEEE Conference on Decision and Control (CDC, p. 3412–3417. [5] Z. Vale, H. Morais, M. Silva, y C. Ramos, “Towards a future scada,” in 2009 IEEE Power and Energy Society General Meeting, PES ’09. [6] Y.-L. Huang, A. A. C´ardenas, S. Amin, Z.-S. Lin, H.-Y. Tsai, y S. Sastry, “Understanding the physical and economic consequences of attacks on control systems,” International Journal of Critical Infrastructure Protection, vol. 2, no. 3, pp. 73–83, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S1874548209000213 [7] L. Hu, Z. Wang, y W. Naeem, “Security analysis of stochastic networked control systems under false data injection attacks,” in 2016 UKACC International Conference on Control, UKACC Control 2016. [8] H. Ge, D. Yue, X. Xie, S. Deng, y Y. Zhang, “Analysis of cyber physical systems security via networked attacks,” in 2017 36th Chinese Control Conference (CCC, p. 4266–4272. [9] X. Jin y W. Haddad, “An adaptive control architecture for leader-follower multiagent systems with stochastic disturbances and sensor and actuator attacks,” in 2018 Annual American Control Conference (ACC, p. 980–985. [10] S. Reba¨ı, H. Voos, y M. Darouach, “A contribution to cyber-security of networ-ked control systems: An event-based control approach,” in 2017 3rd Internatio-nal Conference on Event-Based Control, Communication and Signal Processing (EBCCSP, p. 1–7. [11] A. Al-Wosabi y Z. Shukur, “Software tampering detection in embedded systems - a systematic literature review,” J. Theor. Appl. Inf. Technol, vol. 76, pp. 211–221,. [12] W. Knowles, D. Prince, D. Hutchison, J. Disso, y K. Jones, “A survey of cyber security management in industrial control systems,” Int. J. Crit. Infrastruct. Prot, vol. 9, pp. 52–80,. [13] H. Orojloo y M. Azgomi, “A method for evaluating the consequence propagation of security attacks in cyber–physical systems,” Futur. Gener. Comput. Syst, vol. 67, pp. 57–71,. [14] J. Chapman, S. Ofner, y P. Pauksztelo, “Key factors in industrial control system security,” in 2016 IEEE 41st Conference on Local Computer Networks (LCN, p. 551–554. [15] G. Bernieri, M. Conti, y F. Pascucci, “A novel architecture for cyber-physical security in industrial control networks,” in 2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI, p. 1–6. [16] P.-Y. Chen, S. Yang, y J. McCann, “Distributed real-time anomaly detection in networked industrial sensing systems,” IEEE Trans. Ind. Electron, vol. 62, pp. 1,. [17] X. Zhai, “Exploring icmetrics to detect abnormal program behaviour on embedded devices,” J. Syst. Archit, vol. 61, no. 10, pp. 567–575,. [18] Y. Chen, C. Poskitt, y J. Sun, “Learning from mutants: Using code mutation to learn and monitor invariants of a cyber-physical system,” in Proc. - IEEE Symp. Secur. Priv, vol. 2018-May, pp. 648–660,. [19] A. A. C. L. F. C´ombita, J. Giraldo y N. Quijano, “Response and reconfiguration of cyber-physical control systems: A survey,” in 2015 IEEE 2nd Colombian Conference on Automatic Control (CCAC, p. 1–6. [20] M. Krotofil, J. Larsen, y D. Gollmann, “The process matters: Ensuring data veracity in cyber-physical systems,” in ASIACCS 2015 - Proc. 10th ACM Symp. Information, Comput. Commun. Secur, pp. 133–144,. [21] M. Ahmadian, M. Shajari, y M. Shafiee, “Industrial control system security taxonomic framework with application to a comprehensive incidents survey,” Int. J. Crit. Infrastruct. Prot, vol. 29, pp. 100 356,. [22] N. Falliere, L. Murchu, y E. Chien, “W32. stuxnet dossier,” Symantec Secur. Response, vol. 14, no. February, pp. 1–69,. [23] R. Langner, “Stuxnet: Dissecting a cyberwarfare weapon,” IEEE Secur. Priv, vol. 9, no. 3, pp. 49–51,. [24] D. Zhang, P. Shi, Q.-G. Wang, y L. Yu, “Analysis and synthesis of networked control systems: A survey of recent advances and challenges,” ISA Trans, vol. 66, pp. 376–392,. [25] Y. Ashibani y Q. Mahmoud, “Cyber physical systems security: Analysis, challenges and solutions,” Comput. Secur, vol. 68, pp. 81–97,. [26] H. Mora, J. Colom, D. Gil, y A. Jimeno-Morenilla, “Distributed computational model for shared processing on cyber-physical system environments,” Comput. Commun, vol. 111, pp. 68–83,. [27] H. Karimipour y V. Dinavahi, “Robust massively parallel dynamic state estimation of power systems against cyber-attack,” IEEE Access, vol. 6, pp. 2984–2995,. [28] E. Lee, “The past, present y future of cyber-physical systems: A focus on models,” Sensors, vol. 15, no. 3, pp. 4837–4869,. [29] P. Mosterman y J. Zander, “Cyber-physical systems challenges: a needs analy-sis for collaborating embedded software systems,” Softw. Syst. Model, vol. 15, no. 1, pp. 5–16,. [30] K. Stouffer, V. Pillitteri, S. Lightman, M. Abrams, y A. Hahn, “Nist special publication 800-82: Guide to industrial control systems (ics) security.” [31] L. Sha, “Real time scheduling theory: A historical perspective,” Real-Time Syst, vol. 28, no. 2-3 SPEC. ISS., pp. 101–155,. [32] J. Liu, “Real- time systems.” [33] M. Spuri, “Holistic Analysis for Deadline Scheduled Real-Time Distributed Systems,” INRIA, Research Report RR-2873, 1996, projet REFLECS. [Online]. Available: https://hal.inria.fr/inria-00073818 [34] N. Audsley, A. Burns, M. Richardson, K. Tindell, y A. J. Wellings, “Applying new scheduling theory to static priority pre-emptive scheduling,” Software Engineering Journal, vol. 8, pp. 284–292, 1993. [35] P. Albertos, A. Crespo, I. Ripoll, M. Valles, y P. Balbastre, “Rt control scheduling to reduce control performance degrading,” in Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), vol. 5, 2000, pp. 4889–4894 vol.5. [36] A. Deorankar y S. Thakare, “Survey on anomaly detection of (iot)- internet of things cyberattacks using machine learning,” in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC, p. 115–117. [37] R. Mitchell y I.-R. Chen, “A survey of intrusion detection techniques for cyberphysical systems,” ACM Comput. Surv, vol. 46. [38] L. Cao, X. Jiang, Y. Zhao, S. Wang, D. You, y X. Xu, “A survey of network attacks on cyber-physical systems,” IEEE Access, vol. 8, pp. 44 219–44 227,. [39] B. Zarpel˜ao, R. Miani, C. Kawakani, y S. Alvarenga, “A survey of intrusion detection in internet of things,” J. Netw. Comput. Appl, vol. 84, pp. 25–37,. [40] S. Tan, J. Guerrero, P. Xie, R. Han, y J. Vasquez, “Brief survey on attack detection methods for cyber-physical systems,” IEEE Syst. J, pp. 1–11,. [41] J. Yaacoub, O. Salman, H. Noura, N. Kaaniche, A. Chehab, y M. Malli, “Cyberphysical systems security: Limitations, issues and future trends,” Microprocess. Microsyst, vol. 77, pp. 103 201,. [42] H. Noura, D. Theilliol, J.-C. Ponsart, y A. Chamseddine, “Fault-tolerant control systems: Design and practical applications.” [43] I. Samy, I. Postlethwaite, y D. Gu, “Detection and accommodation of sensor faults in uavs - a comparison of nn and ekf based approaches,” in Proceedings of the IEEE Conference on Decision and Control, p. 4365–4372. [44] V. Justin, N. Marathe, y N. Dongre, “Hybrid ids using svm classifier for detecting dos attack in manet application,” in 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC, p. 775–778. [45] D. Zhang, D. Tang, L. Tang, R. Dai, J. Chen, y N. Zhu, “PCA-SVM-Based Approach of Detecting Low-Rate DoS Attack,” in 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS, p. 1163–1170. [46] A. Theissler, “Anomaly detection in recordings from in-vehicle networks.” [47] D. Dudek, “Collaborative detection of traffic anomalies using first order markov chains,” in 2012 Ninth International Conference on Networked Sensing (INSS, p. 1–4. [48] P. Alpano, J. Pedrasa, y R. Atienza, “Multilayer perceptron with binary weights and activations for intrusion detection of cyber-physical systems,” in IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2017-December, p. 2825–2829. [49] F. Farivar, M. Haghighi, A. Jolfaei, y M. Alazab, “Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber- Physical Systems and Industrial IoT,” IEEE Trans. Ind. Informatics, vol. 16, no. 4, pp. 2716–2725,. [50] J. Shin, Y. Baek, J. Lee, y S. Lee, “Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems.” [51] D. Shi, W. Fan, Y. Xiao, T. Lin, y C. Xing, “Intelligent scheduling of discrete automated production line via deep reinforcement learning,” Int. J. Prod. Res, vol. 58, no. 11, pp. 3362–3380,. [52] J. Bland, M. Petty, T. Whitaker, K. Maxwell, y W. Cantrell, “Machine learning cyberattack and defense strategies,” Comput. Secur, vol. 92, pp. 101 738,. [53] S. Vieira, W. Pinaya, y A. Mechelli, “Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications,” Neuroscience and Biobehavioral Reviews, vol. 74, pp. 58–75,. [54] M. J. J. Douglass, Book Review: Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd edition by Aur ´elien G ´eron, 2020, vol. 43, no. 3. [55] R. Yamashita, M. Nishio, R. Do, y K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights into Imaging, vol. 9, no. 4, pp. 611–629,. [56] B. Polyak, “Some methods of speeding up the convergence of iteration methods,” USSR Computational Mathematics and Mathematical Physics, vol. 4, no. 5, pp. 1–17, 1964. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ 0041555364901375 [57] Y. Nesterov, “A method for unconstrained convex minimization problem with the rate of convergence o(1/k2),” 1983. [58] J. Duchi, E. Hazan, y Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” Journal of Machine Learning Research, vol. 12, pp. 2121–2159, 07 2011. [59] D. Kingma y J. Ba, “Adam: A method for stochastic optimization,” International Conference on Learning Representations, 12 2014. [60] X. Fang, M. Xu, S. Xu, y P. Zhao, “A deep learning framework for predicting cyber attacks rates,” Eurasip J. Inf. Secur, vol. 2019, no. 1. [61] T. Lee, V. P. Singh, y K. H. Cho, “Deep Learning for Time Series,” pp. 107–131, 2021. [62] F. Bianchi, E. Maiorino, M. Kampffmeyer, A. Rizzi, y R. Jenssen, “An overview and comparative analysis of recurrent neural networks for short term load forecasting.” [63] H. P. Breivold, A. Jansen, K. Sandstr¨om, y I. Crnkovic, “Virtualize for architecture sustainability in industrial automation,” in 2013 IEEE 16th International Conference on Computational Science and Engineering, 2013, pp. 409–415. [64] “International Society of Automation (ISA), ANSI/ISA-95.00.01-2000,” Enterprise- Control System Integration - Part 1-5, Tech. Rep., 2007. [65] F. Hofer, M. Sehr, A. Iannopollo, I. Ugalde, A. Sangiovanni-Vincentelli, y B. Russo, “Industrial control via application containers: Migrating from bare-metal to iaas,” in Proc. Int. Conf. Cloud Comput. Technol. Sci. CloudCom, vol. 2019-Decem, pp. 62–69,. [66] T. Goldschmidt, S. Hauck-Stattelmann, S. Malakuti, y S. Gr¨uner, “Containerbased architecture for flexible industrial control applications,” Journal of Systems Architecture, vol. 84, pp. 28–36, 2018. [Online]. Available: https://www. sciencedirect.com/science/article/pii/S1383762117304988 [67] M. Caliskan, M. Ozsiginan, y E. Kugu, “Benefits of the virtualization technologies with intrusion detection and prevention systems,” in AICT 2013 - 7th International Conference on Application of Information and Communication Technologies, Conference Proceedings. [68] Z. Gu y Q. Zhao, “A state-of-the-art survey on real-time issues in embedded systems virtualization,” J. Softw. Eng. Appl, vol. 5, pp. 277–290,. [69] Y. Bock, J. Broeckhove, y P. Hellinckx, “Hierarchical real-time multi-core scheduling through virtualization: A survey,” in 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, vol. 3PGCIC, p. 611–616. |
| dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Autónoma de Occidente, 2022 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.uri.eng.fl_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.eng.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.creativecommons.spa.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
| rights_invalid_str_mv |
Derechos reservados - Universidad Autónoma de Occidente, 2022 https://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
200 páginas |
| dc.format.mimetype.eng.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Autónoma de Occidente |
| dc.publisher.program.spa.fl_str_mv |
Doctorado en Ingeniería |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ingeniería |
| dc.publisher.place.spa.fl_str_mv |
Santiago de Cali |
| institution |
Universidad Autónoma de Occidente |
| bitstream.url.fl_str_mv |
https://red.uao.edu.co/bitstreams/1e4b3587-5e91-45d7-9647-ba11aac69dd2/download https://red.uao.edu.co/bitstreams/24357d5e-26ad-4f8b-bd54-e42a386920a4/download https://red.uao.edu.co/bitstreams/28b8db02-ee0e-4877-97bf-da4826f8b7cb/download https://red.uao.edu.co/bitstreams/fa683c2a-cebe-4cdf-80be-b97671ca0e6d/download https://red.uao.edu.co/bitstreams/d4679825-4486-462a-a608-b0ceae53f413/download https://red.uao.edu.co/bitstreams/9d8000c4-9227-4c77-be59-bec039945b91/download https://red.uao.edu.co/bitstreams/b403e230-d0d4-41bb-a3c8-1058bdccb9f9/download |
| bitstream.checksum.fl_str_mv |
20b5ba22b1117f71589c7318baa2c560 16ef30ed7f6445ab5f93c73713167757 38ed6c3475da0e5c7f91ae38437f1850 61d6bba9e582ff2f118145fe7493d146 55c7f632528328801392f2ed91ed337b 9c9e95d60ed8a24db1a4b74ccc92b1c8 c0ce63a99b8a21bf8d11d97412162e81 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Digital Universidad Autonoma de Occidente |
| repository.mail.fl_str_mv |
repositorio@uao.edu.co |
| _version_ |
1814260198940868608 |
| spelling |
Martínez Castro, Diegovirtual::3010-1Paredes Valencia, Carlos Mario8257120625861258cc80872af33e0e4f2022-06-22T17:24:46Z2022-06-22T17:24:46Z2022-04https://hdl.handle.net/10614/14003Universidad Autónoma de OccidenteRepositorio Educativo Digitalhttps://red.uao.edu.co/Las aplicaciones emergentes de automatización industrial demandan gran flexibilidad en los sistemas, lo cual se logra con en el aumento de la interconexión entre sus módulos, permitiendo el acceso a toda la información del sistema y la reconfiguración en función de los cambios que se presentan durante su funcionamiento, con el propósito de alcanzar puntos óptimos de operación. Para ello se soportan en el concepto de sistemas ciberfísicos (CPSs, Cyber-physical Systems por sus siglas en inglés), los cuales se caracterizan por la integración de sistemas físicos y digitales para crear productos y procesos inteligentes capaces de transformar las cadenas de valor convencionales, lo que ha dado origen al concepto de Smart Factory. Esta flexibilidad abre una gran brecha que afecta a la seguridad de los sistemas de control ya que los nuevos enlaces de comunicación pueden ser utilizados por personas para generar ataques que produzcan riesgo en estas aplicaciones. Este es un problema reciente en los sistemas de control, que originalmente estaban centralizados y posteriormente se implementaron como sistemas interconectados a través de redes aisladas. Actualmente, para proteger estos sistemas se ha optado por utilizar estrategias que han presentado resultados destacables en otros ambientes, como por ejemplo los ambientes de oficina. Sin embargo, las características de estas aplicaciones no son las mismas y los resultados alcanzados no son los deseados. Esta problemática ha motivado varios esfuerzos que pretenden contribuir desde diferentes enfoques a aumentar la seguridad de los sistemas de control. Se realizó una revisión de las estrategias utilizadas actualmente para el diseño de redes de control seguras, y de las técnicas y tecnologías que buscan detectar ataques en los sistemas de control y contribuir a mitigar el efecto de los mismos. Esta revisión permitió identificar los ataques que tienen mayor frecuencia e impacto en estos sistemas, a partir de lo cual se seleccionaron los ataques que comprometen la integridad de las variables de los sistemas de control y los retrasos en el envío de los mensajes que contienen estos valores, para ser abordados en esta propuesta. Con base en lo anterior, en este trabajo se propuso un procedimiento de diseño de aplicaciones de control soportadas en sistemas ciberfísicos que posibilita la implementación de estrategias de detección y tolerancia de ciberataques, el cual integra un enfoque modular y de fácil adaptación. Este procedimiento permite identificar los diversos componentes del sistema los cuales se establecen como microservicios, e integra un planteamiento para evaluar la planificabilidad de los componentes de la aplicación de control. Las etapas del procedimiento detallan el desarrollo de sistemas de detección y aislamiento de ciberataques que son usados para generar alarmas, a partir de las cuales es posible definir qué elemento del sistema está siendo afectado, posibilitando el uso de estrategias soportadas en réplicas de componentes que permitan el reemplazo de los mismos para tolerar ciberataques. La arquitectura y el procedimiento propuesto permiten el cumplimiento de los requisitos del sistema, y presentan un enfoque modular y de fácil adaptabilidad para el diseño.Emerging industrial automation applications demand great flexibility in the systems, which is achieved by increasing the interconnection between its modules, allowing access to all system information and reconfiguration according to changes that occur during operation, in order to achieve optimal operating points. This is supported by the concept of Cyber-physical Systems (CPSs), which are characterized by the integration of physical and digital systems to create intelligent products and processes capable of transforming conventional value chains, which has given rise to the concept of Smart Factory. This flexibility opens a big gap that affects the security of control systems because the new communication links can be used by individuals to generate attacks that produce risk in these applications. This is a recent problem in control systems, which were originally centralized and later implemented as interconnected systems through isolated networks. Currently, to protect these systems have chosen to use strategies that have presented remarkable results in other environments, such as office environments. However, the characteristics of these applications are not the same and the results achieved are not the desired ones. This problem has motivated several efforts that aim to contribute from different approaches to increase the security of control systems. A review was made of the strategies currently used for the design of secure control networks, and of the techniques and technologies that seek to detect attacks on control systems and contribute to mitigate their effect. This review made it possible to identify the attacks that have the greatest frequency and impact on these systems, from which attacks that compromise the integrity of control system variables and delays in sending messages containing these values were selected to be addressed in this proposal. Based on the above, this work proposed a procedure for the design of control applications supported by cyber-physical systems that enables the implementation of cyber-attack detection and tolerance strategies, which integrates a modular and easily adaptable approach. This procedure identifies the various components of the system, which are established as microservices, and integrates an approach to evaluate the plannability of the components of the control application. The stages of the procedure detail the development of cyber-attack detection and isolation systems that are used to generate alarms, from which it is possible to define which element of the system is being affected, enabling the use of strategies supported by replicas of components that allow their replacement to tolerate cyber-attacks. The proposed architecture and procedure allow the system requirements to be met, and present a modular and easily adaptable approach to design.Tesis (Doctor en Ingeniera)-- Universidad Autónoma de Occidente, 2022DoctoradoDoctor(a) en Ingeniería200 páginasapplication/pdfspaUniversidad Autónoma de OccidenteDoctorado en IngenieríaFacultad de IngenieríaSantiago de CaliDerechos reservados - Universidad Autónoma de Occidente, 2022https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Doctorado en IngenieríaCiberinteligencia (Seguridad informática)CiberterrorismoComputación ubicuaCyber intelligence (Computer security)CyberterrorismUbiquitous computingCiberataquesSistema ciberfísicoSistema de detección de ciberataquesCyber-attacksCyber-physical systemCyber-attack detection systemProcedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticosTrabajo de grado - Doctoradohttp://purl.org/coar/resource_type/c_db06Textinfo:eu-repo/semantics/doctoralThesishttp://purl.org/coar/version/c_71e4c1898caa6e32Paredes Valencia, C. M. (2022). Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos. Cali. (Tesis). Universidad Autónoma de Occidente. Cali. Colombia[1] A. Humayed, J. Lin, F. Li, y B. Luo, “Cyber-physical systems security - a survey,” IEEE Internet Things J, vol. 4, no. 6, pp. 1802–1831,.[2] A. Cardenas, S. Amin, Z.-S. Lin, Y. Huang, C.-Y. Huang, y S. Sastry, “Attacks against process control systems: Risk assessment, detection, and response,” in Proceedings of the 6th International Symposium on Information, Computer and Communications Security, ASIACCS 2011, p. 355–366.[3] Y. Shoukry, “Smt-based observer design for cyber-physical systems under sensor attacks,” in 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems, ICCPS 2016 - Proceedings.[4] H. Fawzi, P. Tabuada, y S. Diggavi, “Security for control systems under sensor and actuator attacks,” in 2012 IEEE 51st IEEE Conference on Decision and Control (CDC, p. 3412–3417.[5] Z. Vale, H. Morais, M. Silva, y C. Ramos, “Towards a future scada,” in 2009 IEEE Power and Energy Society General Meeting, PES ’09.[6] Y.-L. Huang, A. A. C´ardenas, S. Amin, Z.-S. Lin, H.-Y. Tsai, y S. Sastry, “Understanding the physical and economic consequences of attacks on control systems,” International Journal of Critical Infrastructure Protection, vol. 2, no. 3, pp. 73–83, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S1874548209000213[7] L. Hu, Z. Wang, y W. Naeem, “Security analysis of stochastic networked control systems under false data injection attacks,” in 2016 UKACC International Conference on Control, UKACC Control 2016.[8] H. Ge, D. Yue, X. Xie, S. Deng, y Y. Zhang, “Analysis of cyber physical systems security via networked attacks,” in 2017 36th Chinese Control Conference (CCC, p. 4266–4272.[9] X. Jin y W. Haddad, “An adaptive control architecture for leader-follower multiagent systems with stochastic disturbances and sensor and actuator attacks,” in 2018 Annual American Control Conference (ACC, p. 980–985.[10] S. Reba¨ı, H. Voos, y M. Darouach, “A contribution to cyber-security of networ-ked control systems: An event-based control approach,” in 2017 3rd Internatio-nal Conference on Event-Based Control, Communication and Signal Processing (EBCCSP, p. 1–7.[11] A. Al-Wosabi y Z. Shukur, “Software tampering detection in embedded systems - a systematic literature review,” J. Theor. Appl. Inf. Technol, vol. 76, pp. 211–221,.[12] W. Knowles, D. Prince, D. Hutchison, J. Disso, y K. Jones, “A survey of cyber security management in industrial control systems,” Int. J. Crit. Infrastruct. Prot, vol. 9, pp. 52–80,.[13] H. Orojloo y M. Azgomi, “A method for evaluating the consequence propagation of security attacks in cyber–physical systems,” Futur. Gener. Comput. Syst, vol. 67, pp. 57–71,.[14] J. Chapman, S. Ofner, y P. Pauksztelo, “Key factors in industrial control system security,” in 2016 IEEE 41st Conference on Local Computer Networks (LCN, p. 551–554.[15] G. Bernieri, M. Conti, y F. Pascucci, “A novel architecture for cyber-physical security in industrial control networks,” in 2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI, p. 1–6.[16] P.-Y. Chen, S. Yang, y J. McCann, “Distributed real-time anomaly detection in networked industrial sensing systems,” IEEE Trans. Ind. Electron, vol. 62, pp. 1,.[17] X. Zhai, “Exploring icmetrics to detect abnormal program behaviour on embedded devices,” J. Syst. Archit, vol. 61, no. 10, pp. 567–575,.[18] Y. Chen, C. Poskitt, y J. Sun, “Learning from mutants: Using code mutation to learn and monitor invariants of a cyber-physical system,” in Proc. - IEEE Symp. Secur. Priv, vol. 2018-May, pp. 648–660,.[19] A. A. C. L. F. C´ombita, J. Giraldo y N. Quijano, “Response and reconfiguration of cyber-physical control systems: A survey,” in 2015 IEEE 2nd Colombian Conference on Automatic Control (CCAC, p. 1–6.[20] M. Krotofil, J. Larsen, y D. Gollmann, “The process matters: Ensuring data veracity in cyber-physical systems,” in ASIACCS 2015 - Proc. 10th ACM Symp. Information, Comput. Commun. Secur, pp. 133–144,.[21] M. Ahmadian, M. Shajari, y M. Shafiee, “Industrial control system security taxonomic framework with application to a comprehensive incidents survey,” Int. J. Crit. Infrastruct. Prot, vol. 29, pp. 100 356,.[22] N. Falliere, L. Murchu, y E. Chien, “W32. stuxnet dossier,” Symantec Secur. Response, vol. 14, no. February, pp. 1–69,.[23] R. Langner, “Stuxnet: Dissecting a cyberwarfare weapon,” IEEE Secur. Priv, vol. 9, no. 3, pp. 49–51,.[24] D. Zhang, P. Shi, Q.-G. Wang, y L. Yu, “Analysis and synthesis of networked control systems: A survey of recent advances and challenges,” ISA Trans, vol. 66, pp. 376–392,.[25] Y. Ashibani y Q. Mahmoud, “Cyber physical systems security: Analysis, challenges and solutions,” Comput. Secur, vol. 68, pp. 81–97,.[26] H. Mora, J. Colom, D. Gil, y A. Jimeno-Morenilla, “Distributed computational model for shared processing on cyber-physical system environments,” Comput. Commun, vol. 111, pp. 68–83,.[27] H. Karimipour y V. Dinavahi, “Robust massively parallel dynamic state estimation of power systems against cyber-attack,” IEEE Access, vol. 6, pp. 2984–2995,.[28] E. Lee, “The past, present y future of cyber-physical systems: A focus on models,” Sensors, vol. 15, no. 3, pp. 4837–4869,.[29] P. Mosterman y J. Zander, “Cyber-physical systems challenges: a needs analy-sis for collaborating embedded software systems,” Softw. Syst. Model, vol. 15, no. 1, pp. 5–16,.[30] K. Stouffer, V. Pillitteri, S. Lightman, M. Abrams, y A. Hahn, “Nist special publication 800-82: Guide to industrial control systems (ics) security.”[31] L. Sha, “Real time scheduling theory: A historical perspective,” Real-Time Syst, vol. 28, no. 2-3 SPEC. ISS., pp. 101–155,.[32] J. Liu, “Real- time systems.”[33] M. Spuri, “Holistic Analysis for Deadline Scheduled Real-Time Distributed Systems,” INRIA, Research Report RR-2873, 1996, projet REFLECS. [Online]. Available: https://hal.inria.fr/inria-00073818[34] N. Audsley, A. Burns, M. Richardson, K. Tindell, y A. J. Wellings, “Applying new scheduling theory to static priority pre-emptive scheduling,” Software Engineering Journal, vol. 8, pp. 284–292, 1993.[35] P. Albertos, A. Crespo, I. Ripoll, M. Valles, y P. Balbastre, “Rt control scheduling to reduce control performance degrading,” in Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), vol. 5, 2000, pp. 4889–4894 vol.5.[36] A. Deorankar y S. Thakare, “Survey on anomaly detection of (iot)- internet of things cyberattacks using machine learning,” in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC, p. 115–117.[37] R. Mitchell y I.-R. Chen, “A survey of intrusion detection techniques for cyberphysical systems,” ACM Comput. Surv, vol. 46.[38] L. Cao, X. Jiang, Y. Zhao, S. Wang, D. You, y X. Xu, “A survey of network attacks on cyber-physical systems,” IEEE Access, vol. 8, pp. 44 219–44 227,.[39] B. Zarpel˜ao, R. Miani, C. Kawakani, y S. Alvarenga, “A survey of intrusion detection in internet of things,” J. Netw. Comput. Appl, vol. 84, pp. 25–37,.[40] S. Tan, J. Guerrero, P. Xie, R. Han, y J. Vasquez, “Brief survey on attack detection methods for cyber-physical systems,” IEEE Syst. J, pp. 1–11,.[41] J. Yaacoub, O. Salman, H. Noura, N. Kaaniche, A. Chehab, y M. Malli, “Cyberphysical systems security: Limitations, issues and future trends,” Microprocess. Microsyst, vol. 77, pp. 103 201,.[42] H. Noura, D. Theilliol, J.-C. Ponsart, y A. Chamseddine, “Fault-tolerant control systems: Design and practical applications.”[43] I. Samy, I. Postlethwaite, y D. Gu, “Detection and accommodation of sensor faults in uavs - a comparison of nn and ekf based approaches,” in Proceedings of the IEEE Conference on Decision and Control, p. 4365–4372.[44] V. Justin, N. Marathe, y N. Dongre, “Hybrid ids using svm classifier for detecting dos attack in manet application,” in 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC, p. 775–778.[45] D. Zhang, D. Tang, L. Tang, R. Dai, J. Chen, y N. Zhu, “PCA-SVM-Based Approach of Detecting Low-Rate DoS Attack,” in 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS, p. 1163–1170.[46] A. Theissler, “Anomaly detection in recordings from in-vehicle networks.”[47] D. Dudek, “Collaborative detection of traffic anomalies using first order markov chains,” in 2012 Ninth International Conference on Networked Sensing (INSS, p. 1–4.[48] P. Alpano, J. Pedrasa, y R. Atienza, “Multilayer perceptron with binary weights and activations for intrusion detection of cyber-physical systems,” in IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2017-December, p. 2825–2829.[49] F. Farivar, M. Haghighi, A. Jolfaei, y M. Alazab, “Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber- Physical Systems and Industrial IoT,” IEEE Trans. Ind. Informatics, vol. 16, no. 4, pp. 2716–2725,.[50] J. Shin, Y. Baek, J. Lee, y S. Lee, “Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems.”[51] D. Shi, W. Fan, Y. Xiao, T. Lin, y C. Xing, “Intelligent scheduling of discrete automated production line via deep reinforcement learning,” Int. J. Prod. Res, vol. 58, no. 11, pp. 3362–3380,.[52] J. Bland, M. Petty, T. Whitaker, K. Maxwell, y W. Cantrell, “Machine learning cyberattack and defense strategies,” Comput. Secur, vol. 92, pp. 101 738,.[53] S. Vieira, W. Pinaya, y A. Mechelli, “Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications,” Neuroscience and Biobehavioral Reviews, vol. 74, pp. 58–75,.[54] M. J. J. Douglass, Book Review: Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd edition by Aur ´elien G ´eron, 2020, vol. 43, no. 3.[55] R. Yamashita, M. Nishio, R. Do, y K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights into Imaging, vol. 9, no. 4, pp. 611–629,.[56] B. Polyak, “Some methods of speeding up the convergence of iteration methods,” USSR Computational Mathematics and Mathematical Physics, vol. 4, no. 5, pp. 1–17, 1964. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ 0041555364901375[57] Y. Nesterov, “A method for unconstrained convex minimization problem with the rate of convergence o(1/k2),” 1983.[58] J. Duchi, E. Hazan, y Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” Journal of Machine Learning Research, vol. 12, pp. 2121–2159, 07 2011.[59] D. Kingma y J. Ba, “Adam: A method for stochastic optimization,” International Conference on Learning Representations, 12 2014.[60] X. Fang, M. Xu, S. Xu, y P. Zhao, “A deep learning framework for predicting cyber attacks rates,” Eurasip J. Inf. Secur, vol. 2019, no. 1.[61] T. Lee, V. P. Singh, y K. H. Cho, “Deep Learning for Time Series,” pp. 107–131, 2021.[62] F. Bianchi, E. Maiorino, M. Kampffmeyer, A. Rizzi, y R. Jenssen, “An overview and comparative analysis of recurrent neural networks for short term load forecasting.”[63] H. P. Breivold, A. Jansen, K. Sandstr¨om, y I. Crnkovic, “Virtualize for architecture sustainability in industrial automation,” in 2013 IEEE 16th International Conference on Computational Science and Engineering, 2013, pp. 409–415.[64] “International Society of Automation (ISA), ANSI/ISA-95.00.01-2000,” Enterprise- Control System Integration - Part 1-5, Tech. Rep., 2007.[65] F. Hofer, M. Sehr, A. Iannopollo, I. Ugalde, A. Sangiovanni-Vincentelli, y B. Russo, “Industrial control via application containers: Migrating from bare-metal to iaas,” in Proc. Int. Conf. Cloud Comput. Technol. Sci. CloudCom, vol. 2019-Decem, pp. 62–69,.[66] T. Goldschmidt, S. Hauck-Stattelmann, S. Malakuti, y S. Gr¨uner, “Containerbased architecture for flexible industrial control applications,” Journal of Systems Architecture, vol. 84, pp. 28–36, 2018. [Online]. Available: https://www. sciencedirect.com/science/article/pii/S1383762117304988[67] M. Caliskan, M. Ozsiginan, y E. Kugu, “Benefits of the virtualization technologies with intrusion detection and prevention systems,” in AICT 2013 - 7th International Conference on Application of Information and Communication Technologies, Conference Proceedings.[68] Z. Gu y Q. Zhao, “A state-of-the-art survey on real-time issues in embedded systems virtualization,” J. Softw. Eng. Appl, vol. 5, pp. 277–290,.[69] Y. Bock, J. Broeckhove, y P. Hellinckx, “Hierarchical real-time multi-core scheduling through virtualization: A survey,” in 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, vol. 3PGCIC, p. 611–616.Publication16469e35-6f18-4e0c-acfe-e8a2e314fedfvirtual::3010-116469e35-6f18-4e0c-acfe-e8a2e314fedfvirtual::3010-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000195928virtual::3010-1LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/1e4b3587-5e91-45d7-9647-ba11aac69dd2/download20b5ba22b1117f71589c7318baa2c560MD52ORIGINALT10131_Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos.pdfT10131_Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos.pdfTexto archivo completo del trabajo de grado, PDFapplication/pdf2218785https://red.uao.edu.co/bitstreams/24357d5e-26ad-4f8b-bd54-e42a386920a4/download16ef30ed7f6445ab5f93c73713167757MD53TA10131_Autorización trabajo de grado.pdfTA10131_Autorización trabajo de grado.pdfAutorización publicación del trabajo de gradoapplication/pdf77249https://red.uao.edu.co/bitstreams/28b8db02-ee0e-4877-97bf-da4826f8b7cb/download38ed6c3475da0e5c7f91ae38437f1850MD54TEXTT10131_Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos.pdf.txtT10131_Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos.pdf.txtExtracted texttext/plain398822https://red.uao.edu.co/bitstreams/fa683c2a-cebe-4cdf-80be-b97671ca0e6d/download61d6bba9e582ff2f118145fe7493d146MD55TA10131_Autorización trabajo de grado.pdf.txtTA10131_Autorización trabajo de grado.pdf.txtExtracted texttext/plain4080https://red.uao.edu.co/bitstreams/d4679825-4486-462a-a608-b0ceae53f413/download55c7f632528328801392f2ed91ed337bMD57THUMBNAILT10131_Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos.pdf.jpgT10131_Procedimiento de diseño de sistemas ciberfísicos de tiempo real tolerantes a ataques cibernéticos.pdf.jpgGenerated Thumbnailimage/jpeg5831https://red.uao.edu.co/bitstreams/9d8000c4-9227-4c77-be59-bec039945b91/download9c9e95d60ed8a24db1a4b74ccc92b1c8MD56TA10131_Autorización trabajo de grado.pdf.jpgTA10131_Autorización trabajo de grado.pdf.jpgGenerated Thumbnailimage/jpeg12247https://red.uao.edu.co/bitstreams/b403e230-d0d4-41bb-a3c8-1058bdccb9f9/downloadc0ce63a99b8a21bf8d11d97412162e81MD5810614/14003oai:red.uao.edu.co:10614/140032024-03-07 16:53:08.515https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - Universidad Autónoma de Occidente, 2022open.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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 |