Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning

Establecer un sistema de análisis de tráfico de red basado en algoritmos de machine learning (ML), orientado a sistemas de control industrial que permita: la identificación de comportamientos anormales para evitar la explotación de vulnerabilidades que afecten la seguridad de procesos industriales r...

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Fecha de publicación:: 2023

Institución:: Universidad del Rosario

Repositorio:: Repositorio EdocUR - U. Rosario

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dc.title.none.fl_str_mv	Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning
dc.title.TranslatedTitle.none.fl_str_mv	Detection of anomalies in red traffic of Industrial Control Systems supported by machine learning algorithms
title	Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning
spellingShingle	Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning Machine learning Sistemas de control industrial ICS Tráfico de red industrial Detección de anomalías Reducción de riesgos en seguridad de procesos industriales Machine Learning Cibersecurity
title_short	Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning
title_full	Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning
title_fullStr	Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning
title_full_unstemmed	Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning
title_sort	Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning
dc.contributor.advisor.none.fl_str_mv	Díaz López, Daniel Orlando
dc.subject.none.fl_str_mv	Machine learning Sistemas de control industrial ICS Tráfico de red industrial Detección de anomalías Reducción de riesgos en seguridad de procesos industriales
topic	Machine learning Sistemas de control industrial ICS Tráfico de red industrial Detección de anomalías Reducción de riesgos en seguridad de procesos industriales Machine Learning Cibersecurity
dc.subject.keyword.none.fl_str_mv	Machine Learning Cibersecurity
description	Establecer un sistema de análisis de tráfico de red basado en algoritmos de machine learning (ML), orientado a sistemas de control industrial que permita: la identificación de comportamientos anormales para evitar la explotación de vulnerabilidades que afecten la seguridad de procesos industriales reduciendo riesgos de disponibilidad y soporte la continuidad del negocio.
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-03-24T21:27:37Z
dc.date.available.none.fl_str_mv	2023-03-24T21:27:37Z
dc.date.created.none.fl_str_mv	2023-02-07
dc.type.none.fl_str_mv	bachelorThesis
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dc.type.document.none.fl_str_mv	Trabajo de grado
dc.type.spa.none.fl_str_mv	Trabajo de grado
dc.identifier.doi.none.fl_str_mv	https://doi.org/10.48713/10336_38272
dc.identifier.uri.none.fl_str_mv	https://repository.urosario.edu.co/handle/10336/38272
url	https://doi.org/10.48713/10336_38272 https://repository.urosario.edu.co/handle/10336/38272
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dc.format.extent.none.fl_str_mv	78 pp
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dc.publisher.spa.fl_str_mv	Universidad del Rosario
dc.publisher.department.spa.fl_str_mv	Escuela de Ingeniería, Ciencia y Tecnología
dc.publisher.program.spa.fl_str_mv	Maestría en Matemáticas Aplicadas y Ciencias de la Computación
institution	Universidad del Rosario
dc.source.bibliographicCitation.none.fl_str_mv	V. Atluri and J. Horne, "A Machine Learning based Threat Intelligence Framework for Industrial Control System Network Traffic Indicators of Compromise," SoutheastCon 2021, 2021, pp. 1-5, doi: 10.1109/SoutheastCon45413.2021.9401809. J. M. Beaver, R. C. Borges-Hink and M. A. Buckner, "An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications," 2013 12th International Conference on Machine Learning and Applications, 2013, pp. 54-59, doi: 10.1109/ICMLA.2013.105. H. Lan, X. Zhu, J. Sun and S. Li, "Traffic Data Classification to Detect Man-in-the-Middle Attacks in Industrial Control System," 2019 6th International Conference on Dependable Systems and Their Applications (DSA), 2020, pp. 430-434, doi: 10.1109/DSA.2019.00067. S. M. Rachmawati, D. -S. Kim and J. -M. Lee, "Machine Learning Algorithm in Network Traffic Classification," 2021 International Conference on Information and Communication Technology Convergence (ICTC), 2021, pp. 1010-1013, doi: 10.1109/ICTC52510.2021.9620746. S. P. Khedkar and R. AroulCanessane, "Machine Learning Model for classification of IoT Network Traffic," 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2020, pp. 166-170, doi: 10.1109/I-SMAC49090.2020.9243468. H. Li and S. Qin, "Optimization and implementation of industrial control system network intrusion detection by telemetry analysis," 2017 3rd IEEE International Conference on Computer and Communications (ICCC), 2017, pp. 1251-1254, doi: 10.1109/CompComm.2017.8322743. H. Singh, "Performance Analysis of Unsupervised Machine Learning Techniques for Network Traffic Classification," 2015 Fifth International Conference on Advanced Computing & Communication Technologies, 2015, pp. 401-404, doi: 10.1109/ACCT.2015.54. Chi-Ho Tsang and S. Kwong, "Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction," 2005 IEEE International Conference on Industrial Technology, 2005, pp. 51-56, doi: 10.1109/ICIT.2005.1600609. E. D. Knapp, J.T. Langill, “Industrial Network Security, Securing Critical Infrastructure Networks for Smart Grid, SCADA, and Other Industrial Control Systems”, ISBN: 978-0-12-420114-9. W. Chen, T. Liu, Y. Tang, D. Xu, “Multi-level adaptive coupled method for industrial control networks safety based on machine learning”, Safety Science, Volume 120, 2019, Pages 268-275, ISSN 0925-7535, https://doi.org/10.1016/j.ssci.2019.07.012. E. Anthi, L. Williams, M. Rhode, P. Burnap, A.Wedgbury, “Adversarial attacks on machine learning cybersecurity defences in Industrial Control Systems”, Journal of Information Security and Applications, Volume 58, 2021, 102717, ISSN 2214-2126, https://doi.org/10.1016/j.jisa.2020.102717. J. Pei, K. Zhong, M. Ahmad Jan, J. Li, “Personalized federated learning framework for network traffic anomaly detection”, Computer Networks, Volume 209, 2022, 108906, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2022.108906. A. Shahraki, M. Abbasi, A. Taherkordi, A.Delia Jurcut, “A comparative study on online machine learning techniques for network traffic streams analysis”, Computer Networks, Volume 207, 2022, 108836, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2022.108836. J. Vávra, M. Hromada, L. Lukáš, J.Dworzecki, “Adaptive anomaly detection system based on machine learning algorithms in an industrial control environment, International Journal of Critical Infrastructure Protection”, Volume 34, 2021, 100446, ISSN 1874-5482, https://doi.org/10.1016/j.ijcip.2021.100446. M. A. Umer, K. N. Junejo, M. T. Jilani, A. P. Mathur, “Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations”, International Journal of Critical Infrastructure Protection, Volume 38, 2022, 100516, ISSN 1874-5482, https://doi.org/10.1016/j.ijcip.2022.100516. I. Chakraborty, B. M. Kelley, B. Gallagher, “Industrial control system device classification using network traffic features and neural network embeddings”, Array, Volume 12, 2021, 100081, ISSN 2590-0056, https://doi.org/10.1016/j.array.2021.100081. Yask & B. Suresh Kumar (2019), “A review of model on malware detection and protection for the distributed control systems (Industrial control systems) in oil & gas sectors”, Journal of Discrete Mathematical Sciences and Cryptography, 22:4, 531-540, DOI: 10.1080/09720529.2019.1642623 J. F. Brenner (2013), “Eyes wide shut: The growing threat of cyber-attacks on industrial control systems”, Bulletin of the Atomic Scientists, 69:5, 15-20, DOI: 10.1177/0096340213501372 S. Bagui, X. Fang, E. Kalaimannan, S.C. Bagui & Joseph Sheehan (2017), “Comparison of machine-learning algorithms for classification of VPN network traffic flow using time-related features”, Journal of Cyber Security Technology, 1:2, 108-126, DOI: 10.1080/23742917.2017.1321891 P. Ackerman, “Industrial Cybersecurity, Efficiently secure critical infrastructure systems”, Published by Packt Publishing Ltd, ISBN 978-1-78839-515-1. J. McCarthy, E Division, D Faatz, “Securing the Industrial Internet of Things: Cybersecurity for Distributed Energy Resources”, NIST SPECIAL PUBLICATION 1800-32, National Institute of Standards and Technology, https://www.nccoe.nist.gov/iiot
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spelling	Díaz López, Daniel Orlando1061695713600Tristancho Muñoz, Miguel AngelMagíster en Matemáticas Aplicadas y Ciencias de la ComputaciónFull timef8c9d57c-260c-4e7f-bc8b-ab7b40b5a50c-12023-03-24T21:27:37Z2023-03-24T21:27:37Z2023-02-07Establecer un sistema de análisis de tráfico de red basado en algoritmos de machine learning (ML), orientado a sistemas de control industrial que permita: la identificación de comportamientos anormales para evitar la explotación de vulnerabilidades que afecten la seguridad de procesos industriales reduciendo riesgos de disponibilidad y soporte la continuidad del negocio.The growing development of computer networks associated with industrial systems and their integration with corporate networks (Internet) have made this group a desired target for cybercriminals worldwide. Mitigating this type of risk is one of the highest priorities for integrators, manufacturers, and users of control systems due to the great impact that can occur on the economy, the environment and the people in an organization when materialization occurs. of an attempted attack or sabotage of industrial processes. It is becoming increasingly important for industrial organizations to become aware of the weakness of these systems and seek organizational structures for security management that help them optimize their protection against external threats from all points of view to detect and address incidents. security-related issues before they become a major problem.78 ppapplication/pdfhttps://doi.org/10.48713/10336_38272 https://repository.urosario.edu.co/handle/10336/38272spaUniversidad del RosarioEscuela de Ingeniería, Ciencia y TecnologíaMaestría en Matemáticas Aplicadas y Ciencias de la ComputaciónAttribution-NonCommercial-NoDerivatives 4.0 InternationalAbierto (Texto Completo)http://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2V. Atluri and J. Horne, "A Machine Learning based Threat Intelligence Framework for Industrial Control System Network Traffic Indicators of Compromise," SoutheastCon 2021, 2021, pp. 1-5, doi: 10.1109/SoutheastCon45413.2021.9401809.J. M. Beaver, R. C. Borges-Hink and M. A. Buckner, "An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications," 2013 12th International Conference on Machine Learning and Applications, 2013, pp. 54-59, doi: 10.1109/ICMLA.2013.105.H. Lan, X. Zhu, J. Sun and S. Li, "Traffic Data Classification to Detect Man-in-the-Middle Attacks in Industrial Control System," 2019 6th International Conference on Dependable Systems and Their Applications (DSA), 2020, pp. 430-434, doi: 10.1109/DSA.2019.00067.S. M. Rachmawati, D. -S. Kim and J. -M. Lee, "Machine Learning Algorithm in Network Traffic Classification," 2021 International Conference on Information and Communication Technology Convergence (ICTC), 2021, pp. 1010-1013, doi: 10.1109/ICTC52510.2021.9620746.S. P. Khedkar and R. AroulCanessane, "Machine Learning Model for classification of IoT Network Traffic," 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2020, pp. 166-170, doi: 10.1109/I-SMAC49090.2020.9243468.H. Li and S. Qin, "Optimization and implementation of industrial control system network intrusion detection by telemetry analysis," 2017 3rd IEEE International Conference on Computer and Communications (ICCC), 2017, pp. 1251-1254, doi: 10.1109/CompComm.2017.8322743.H. Singh, "Performance Analysis of Unsupervised Machine Learning Techniques for Network Traffic Classification," 2015 Fifth International Conference on Advanced Computing & Communication Technologies, 2015, pp. 401-404, doi: 10.1109/ACCT.2015.54.Chi-Ho Tsang and S. Kwong, "Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction," 2005 IEEE International Conference on Industrial Technology, 2005, pp. 51-56, doi: 10.1109/ICIT.2005.1600609.E. D. Knapp, J.T. Langill, “Industrial Network Security, Securing Critical Infrastructure Networks for Smart Grid, SCADA, and Other Industrial Control Systems”, ISBN: 978-0-12-420114-9.W. Chen, T. Liu, Y. Tang, D. Xu, “Multi-level adaptive coupled method for industrial control networks safety based on machine learning”, Safety Science, Volume 120, 2019, Pages 268-275, ISSN 0925-7535, https://doi.org/10.1016/j.ssci.2019.07.012.E. Anthi, L. Williams, M. Rhode, P. Burnap, A.Wedgbury, “Adversarial attacks on machine learning cybersecurity defences in Industrial Control Systems”, Journal of Information Security and Applications, Volume 58, 2021, 102717, ISSN 2214-2126, https://doi.org/10.1016/j.jisa.2020.102717.J. Pei, K. Zhong, M. Ahmad Jan, J. Li, “Personalized federated learning framework for network traffic anomaly detection”, Computer Networks, Volume 209, 2022, 108906, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2022.108906.A. Shahraki, M. Abbasi, A. Taherkordi, A.Delia Jurcut, “A comparative study on online machine learning techniques for network traffic streams analysis”, Computer Networks, Volume 207, 2022, 108836, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2022.108836.J. Vávra, M. Hromada, L. Lukáš, J.Dworzecki, “Adaptive anomaly detection system based on machine learning algorithms in an industrial control environment, International Journal of Critical Infrastructure Protection”, Volume 34, 2021, 100446, ISSN 1874-5482, https://doi.org/10.1016/j.ijcip.2021.100446.M. A. Umer, K. N. Junejo, M. T. Jilani, A. P. Mathur, “Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations”, International Journal of Critical Infrastructure Protection, Volume 38, 2022, 100516, ISSN 1874-5482, https://doi.org/10.1016/j.ijcip.2022.100516.I. Chakraborty, B. M. Kelley, B. Gallagher, “Industrial control system device classification using network traffic features and neural network embeddings”, Array, Volume 12, 2021, 100081, ISSN 2590-0056, https://doi.org/10.1016/j.array.2021.100081.Yask & B. Suresh Kumar (2019), “A review of model on malware detection and protection for the distributed control systems (Industrial control systems) in oil & gas sectors”, Journal of Discrete Mathematical Sciences and Cryptography, 22:4, 531-540, DOI: 10.1080/09720529.2019.1642623J. F. Brenner (2013), “Eyes wide shut: The growing threat of cyber-attacks on industrial control systems”, Bulletin of the Atomic Scientists, 69:5, 15-20, DOI: 10.1177/0096340213501372S. Bagui, X. Fang, E. Kalaimannan, S.C. Bagui & Joseph Sheehan (2017), “Comparison of machine-learning algorithms for classification of VPN network traffic flow using time-related features”, Journal of Cyber Security Technology, 1:2, 108-126, DOI: 10.1080/23742917.2017.1321891P. Ackerman, “Industrial Cybersecurity, Efficiently secure critical infrastructure systems”, Published by Packt Publishing Ltd, ISBN 978-1-78839-515-1.J. McCarthy, E Division, D Faatz, “Securing the Industrial Internet of Things: Cybersecurity for Distributed Energy Resources”, NIST SPECIAL PUBLICATION 1800-32, National Institute of Standards and Technology, https://www.nccoe.nist.gov/iiotinstname:Universidad del Rosarioreponame:Repositorio Institucional EdocURMachine learningSistemas de control industrial ICSTráfico de red industrialDetección de anomalíasReducción de riesgos en seguridad de procesos industrialesMachine LearningCibersecurityDetección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learningDetection of anomalies in red traffic of Industrial Control Systems supported by machine learning algorithmsbachelorThesisTrabajo de gradoTrabajo de gradohttp://purl.org/coar/resource_type/c_7a1fORIGINALDeteccion-de-anomalías-en- trafico-de-red-de-Sistemas-de-Control.pdfDeteccion-de-anomalías-en- trafico-de-red-de-Sistemas-de-Control.pdfapplication/pdf4140457https://repository.urosario.edu.co/bitstreams/59f6d889-25c8-4ddd-9037-6300453ea369/download652fffcac78698da385de1b1591daa06MD51LICENSElicense.txtlicense.txttext/plain1483https://repository.urosario.edu.co/bitstreams/19518c31-6203-4e93-81c6-eb308a553575/downloadb2825df9f458e9d5d96ee8b7cd74fde6MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8899https://repository.urosario.edu.co/bitstreams/75759e88-e10a-4bbd-be13-fb262484ef94/download3b6ce8e9e36c89875e8cf39962fe8920MD53TEXTDeteccion-de-anomalías-en- trafico-de-red-de-Sistemas-de-Control.pdf.txtDeteccion-de-anomalías-en- trafico-de-red-de-Sistemas-de-Control.pdf.txtExtracted texttext/plain101701https://repository.urosario.edu.co/bitstreams/4409010e-c86a-4142-9b04-c454a0de042d/download18e83963b41bdf1db7cc2aae874eef94MD54THUMBNAILDeteccion-de-anomalías-en- trafico-de-red-de-Sistemas-de-Control.pdf.jpgDeteccion-de-anomalías-en- trafico-de-red-de-Sistemas-de-Control.pdf.jpgGenerated Thumbnailimage/jpeg2974https://repository.urosario.edu.co/bitstreams/dcd7d6cd-7436-408f-b1e2-56ad9effe909/downloadd065fa3653ab1ff7c2592cacf993acaeMD5510336/38272oai:repository.urosario.edu.co:10336/382722023-03-25 03:03:08.768http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.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

Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning

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