Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment

Este proyecto explora la dinámica de un entorno de redes definidas por software (SDN) en respuesta a varios ciberataques, centrándose en el impacto en el rendimiento y la estabilidad de la red. Utilizando una configuración controlada con Mininet y ONOS, establecimos una topología de red diseñada par...

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Autores:: Cedeño Romero, Nicolas Steven

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2024

Institución:: Universidad Santo Tomás

Repositorio:: Repositorio Institucional USTA

Idioma:: spa

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dc.title.spa.fl_str_mv	Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment
title	Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment
spellingShingle	Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment SDN Network Security Cyberattacks OpenFlow ONOS Ingeniería de Telecomunicaciones Sistema de telecomunicaciones Bases de datos SDN Seguridad de redes Ciberataques Flujo abierto ONOS
title_short	Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment
title_full	Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment
title_fullStr	Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment
title_full_unstemmed	Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment
title_sort	Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment
dc.creator.fl_str_mv	Cedeño Romero, Nicolas Steven
dc.contributor.advisor.none.fl_str_mv	Arevalo Herrera, Juliana
dc.contributor.author.none.fl_str_mv	Cedeño Romero, Nicolas Steven
dc.contributor.orcid.spa.fl_str_mv	https://orcid.org/0009-0009-7119-1127 https://orcid.org/0000-0001-7401-4286
dc.contributor.googlescholar.spa.fl_str_mv	https://scholar.google.com/citations?hl=es&user=qDZbf-wAAAAJ
dc.contributor.cvlac.spa.fl_str_mv	https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0002170070 https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000759813
dc.contributor.corporatename.spa.fl_str_mv	Universidad Santo Tomás
dc.subject.keyword.spa.fl_str_mv	SDN Network Security Cyberattacks OpenFlow ONOS
topic	SDN Network Security Cyberattacks OpenFlow ONOS Ingeniería de Telecomunicaciones Sistema de telecomunicaciones Bases de datos SDN Seguridad de redes Ciberataques Flujo abierto ONOS
dc.subject.lemb.spa.fl_str_mv	Ingeniería de Telecomunicaciones Sistema de telecomunicaciones Bases de datos
dc.subject.proposal.spa.fl_str_mv	SDN Seguridad de redes Ciberataques Flujo abierto ONOS
description	Este proyecto explora la dinámica de un entorno de redes definidas por software (SDN) en respuesta a varios ciberataques, centrándose en el impacto en el rendimiento y la estabilidad de la red. Utilizando una configuración controlada con Mininet y ONOS, establecimos una topología de red diseñada para simular condiciones vulnerables a las amenazas cibernéticas. A través de la implementación del envenenamiento de la topología ARP, se introdujeron tres hosts falsos, comprometiendo la integridad de las comunicaciones de la red. Además, se ejecutó un ataque de inundación de Packet-In, lo que resultó en la falla del controlador ONOS, lo que subrayó los desafíos de administrar el tráfico de red en escenarios de ataque. Los experimentos demostraron cómo se puede explotar la arquitectura de control centralizada de SDN, revelando vulnerabilidades significativas en el manejo del tráfico malicioso. Esta investigación destaca la necesidad crítica de medidas de seguridad mejoradas en las arquitecturas SDN para mitigar los riesgos asociados con las amenazas cibernéticas en evolución, brindando información para futuros desarrollos en estrategias de seguridad de red.
publishDate	2024
dc.date.issued.none.fl_str_mv	2024
dc.date.accessioned.none.fl_str_mv	2025-01-17T17:09:36Z
dc.date.available.none.fl_str_mv	2025-01-17T17:09:36Z
dc.type.local.spa.fl_str_mv	Trabajo de grado
dc.type.version.none.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv	http://purl.org/coar/resource_type/c_7a1f
dc.type.drive.none.fl_str_mv	info:eu-repo/semantics/bachelorThesis
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dc.identifier.citation.spa.fl_str_mv	Cedeño Romero, N.S. (2024). Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11634/59058
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional Universidad Santo Tomás
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Santo Tomás
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identifier_str_mv	Cedeño Romero, N.S. (2024). Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional. reponame:Repositorio Institucional Universidad Santo Tomás instname:Universidad Santo Tomás repourl:https://repository.usta.edu.co
url	http://hdl.handle.net/11634/59058
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	L. Mhamdi and M. M. Isa, “Securing SDN: Hybrid autoencoder-random forest for intrusion detection and attack mitigation,” J. Netw. Comput. Appl., vol. 225, p. 103868, May 2024, doi: 10.1016/j.jnca.2024.103868. M. W. Nadeem, H. G. Goh, Y. Aun, and V. Ponnusamy, “Detecting and Mitigating Botnet Attacks in Software-Defined Networks Using Deep Learning Techniques,” IEEE Access, vol. 11, pp. 49153–49171, 2023, doi: 10.1109/ACCESS.2023.3277397. M. AbdulRaheem et al., “Machine learning assisted snort and zeek in detecting DDoS attacks in software-defined networking,” Int. J. Inf. Technol., vol. 16, no. 3, pp. 1627–1643, Mar. 2023, doi: 10.1007/s41870-023-01469-3. H. U. Ishtiaq, A. A. Bhutta, and A. N. Mian, “DHCP DoS and starvation attacks on SDN controllers and their mitigation,” J. Comput. Virol. Hacking Tech., vol. 20, no. 1, pp. 15–25, Mar. 2023, doi: 10.1007/s11416-023-00483-0. C. Gonzalez and S. M. Charfadine, “SDN Controllers and ML-Based Anomaly Detection in Embedded Systems: A Comparative Analysis,” in Proceedings - 10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023, 2023. doi: 10.1109/WINCOM59760.2023.10322912. I. H. Abdulqadder, D. Zou, and I. T. Aziz, “The DAG blockchain: A secure edge assisted honeypot for attack detection and multi-controller based load balancing in SDN 5G,” Futur. Gener. Comput. Syst., vol. 141, pp. 339–354, Apr. 2023, doi: 10.1016/j.future.2022.11.008. S. Karnani and H. K. Shakya, “Mitigation strategies for distributed denial of service (DDoS) in SDN: A survey and taxonomy,” Information Security Journal, vol. 32, no. 6. Taylor & Francis, pp. 444–468, Nov. 02, 2023. doi: 10.1080/19393555.2022.2111004. M. Yue, Q. Yan, Z. Lu, and Z. Wu, “CCS: A Cross-Plane Collaboration Strategy to Defend Against LDoS Attacks in SDN,” IEEE Trans. Netw. Serv. Manag., 2024, doi: 10.1109/TNSM.2024.3363490. D. Tang, Z. Zheng, C. Yin, B. Xiong, Z. Qin, and Q. Yang, “FTODefender: An efficient flow table overflow attacks defending system in SDN,” Expert Syst. Appl., vol. 237, p. 121460, Mar. 2024, doi: 10.1016/j.eswa.2023.121460. D. Tang, Z. Zheng, K. Li, C. Yin, W. Liang, and J. Zhang, “FTOP: An Efficient Flow Table Overflow Preventing System for Switches in SDN,” IEEE Trans. Netw. Sci. Eng., 2023, doi: 10.1109/TNSE.2023.3297650. N. Aslam, S. Srivastava, and M. M. Gore, “A Comprehensive Analysis of Machine Learning- and Deep Learning-Based Solutions for DDoS Attack Detection in SDN,” Arab. J. Sci. Eng., vol. 49, no. 3, pp. 3533–3573, Mar. 2024, doi: 10.1007/s13369- 023-08075-2 Toyeer-E-Ferdoush, H. Rahman, and M. Hasan, “A convenient way to mitigate DDoS TCP SYN flood attack,” J. Discret. Math. Sci. Cryptogr., vol. 25, no. 7, pp. 2069–2077, Oct. 2022, doi: 10.1080/09720529.2022.2133246. H. Qian and L. Cai, “Improved K-means-based solution for detecting DDoS attacks in SDN,” Phys. Commun., vol. 64, p. 102318, Jun. 2024, doi: 10.1016/j.phycom.2024.102318. Y. Gao and M. Xu, “Defense Against Software-Defined Network Topology Poisoning Attacks,” Tsinghua Sci. Technol., vol. 28, no. 1, pp. 39–46, 2023, doi: 10.26599/TST.2021.9010077. S. Sunil Gowda and R. B. Dayananda, “Detection And Prevention of ARP Attack in Software Defined Networks,” in 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023, 2023. doi: 10.1109/ICCCNT56998.2023.10307123. A. T. Phu et al., “Defending SDN against packet injection attacks using deep learning,” Comput. Networks, vol. 234, p. 109935, Oct. 2023, doi: 10.1016/j.comnet.2023.109935.
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dc.coverage.campus.spa.fl_str_mv	CRAI-USTA Bogotá
dc.publisher.spa.fl_str_mv	Universidad Santo Tomás
dc.publisher.program.spa.fl_str_mv	Pregrado Ingeniería de Telecomunicaciones
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spelling	Arevalo Herrera, Julianawill be generated::orcid::0000-0001-7401-4286600Cedeño Romero, Nicolas Stevenwill be generated::orcid::0009-0009-7119-1127600https://orcid.org/0009-0009-7119-1127https://orcid.org/0000-0001-7401-4286https://scholar.google.com/citations?hl=es&user=qDZbf-wAAAAJhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0002170070https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000759813Universidad Santo Tomás2025-01-17T17:09:36Z2025-01-17T17:09:36Z2024Cedeño Romero, N.S. (2024). Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.http://hdl.handle.net/11634/59058reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coEste proyecto explora la dinámica de un entorno de redes definidas por software (SDN) en respuesta a varios ciberataques, centrándose en el impacto en el rendimiento y la estabilidad de la red. Utilizando una configuración controlada con Mininet y ONOS, establecimos una topología de red diseñada para simular condiciones vulnerables a las amenazas cibernéticas. A través de la implementación del envenenamiento de la topología ARP, se introdujeron tres hosts falsos, comprometiendo la integridad de las comunicaciones de la red. Además, se ejecutó un ataque de inundación de Packet-In, lo que resultó en la falla del controlador ONOS, lo que subrayó los desafíos de administrar el tráfico de red en escenarios de ataque. Los experimentos demostraron cómo se puede explotar la arquitectura de control centralizada de SDN, revelando vulnerabilidades significativas en el manejo del tráfico malicioso. Esta investigación destaca la necesidad crítica de medidas de seguridad mejoradas en las arquitecturas SDN para mitigar los riesgos asociados con las amenazas cibernéticas en evolución, brindando información para futuros desarrollos en estrategias de seguridad de red.This project explores the dynamics of a Software-Defined Networking (SDN) environment in response to various cyberattacks, focusing on the impact on network performance and stability. Utilizing a controlled setup with Mininet and ONOS, we established a network topology designed to simulate conditions vulnerable to cyber threats. Through the implementation of ARP topology poisoning, three fake hosts were introduced, compromising the integrity of network communications. Additionally, a Packet-In flood attack was executed, resulting in the failure of the ONOS controller, which underscored the challenges of managing network traffic under attack scenarios. The experiments demonstrated how SDN's centralized control architecture can be exploited, revealing significant vulnerabilities in handling malicious traffic. This research highlights the critical need for enhanced security measures in SDN architectures to mitigate risks associated with evolving cyber threats, providing insights for future developments in network security strategies.Ingeniero de TelecomunicacionesPregradoapplication/pdfspaUniversidad Santo TomásPregrado Ingeniería de TelecomunicacionesFacultad de Ingeniería de TelecomunicacionesAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data EnvironmentSDNNetwork SecurityCyberattacksOpenFlowONOSIngeniería de TelecomunicacionesSistema de telecomunicacionesBases de datosSDNSeguridad de redesCiberataquesFlujo abiertoONOSTrabajo de gradoinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisCRAI-USTA BogotáL. Mhamdi and M. M. Isa, “Securing SDN: Hybrid autoencoder-random forest for intrusion detection and attack mitigation,” J. Netw. Comput. Appl., vol. 225, p. 103868, May 2024, doi: 10.1016/j.jnca.2024.103868.M. W. Nadeem, H. G. Goh, Y. Aun, and V. Ponnusamy, “Detecting and Mitigating Botnet Attacks in Software-Defined Networks Using Deep Learning Techniques,” IEEE Access, vol. 11, pp. 49153–49171, 2023, doi: 10.1109/ACCESS.2023.3277397.M. AbdulRaheem et al., “Machine learning assisted snort and zeek in detecting DDoS attacks in software-defined networking,” Int. J. Inf. Technol., vol. 16, no. 3, pp. 1627–1643, Mar. 2023, doi: 10.1007/s41870-023-01469-3.H. U. Ishtiaq, A. A. Bhutta, and A. N. Mian, “DHCP DoS and starvation attacks on SDN controllers and their mitigation,” J. Comput. Virol. Hacking Tech., vol. 20, no. 1, pp. 15–25, Mar. 2023, doi: 10.1007/s11416-023-00483-0.C. Gonzalez and S. M. Charfadine, “SDN Controllers and ML-Based Anomaly Detection in Embedded Systems: A Comparative Analysis,” in Proceedings - 10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023, 2023. doi: 10.1109/WINCOM59760.2023.10322912.I. H. Abdulqadder, D. Zou, and I. T. Aziz, “The DAG blockchain: A secure edge assisted honeypot for attack detection and multi-controller based load balancing in SDN 5G,” Futur. Gener. Comput. Syst., vol. 141, pp. 339–354, Apr. 2023, doi: 10.1016/j.future.2022.11.008.S. Karnani and H. K. Shakya, “Mitigation strategies for distributed denial of service (DDoS) in SDN: A survey and taxonomy,” Information Security Journal, vol. 32, no. 6. Taylor & Francis, pp. 444–468, Nov. 02, 2023. doi: 10.1080/19393555.2022.2111004.M. Yue, Q. Yan, Z. Lu, and Z. Wu, “CCS: A Cross-Plane Collaboration Strategy to Defend Against LDoS Attacks in SDN,” IEEE Trans. Netw. Serv. Manag., 2024, doi: 10.1109/TNSM.2024.3363490.D. Tang, Z. Zheng, C. Yin, B. Xiong, Z. Qin, and Q. Yang, “FTODefender: An efficient flow table overflow attacks defending system in SDN,” Expert Syst. Appl., vol. 237, p. 121460, Mar. 2024, doi: 10.1016/j.eswa.2023.121460.D. Tang, Z. Zheng, K. Li, C. Yin, W. Liang, and J. Zhang, “FTOP: An Efficient Flow Table Overflow Preventing System for Switches in SDN,” IEEE Trans. Netw. Sci. Eng., 2023, doi: 10.1109/TNSE.2023.3297650.N. Aslam, S. Srivastava, and M. M. Gore, “A Comprehensive Analysis of Machine Learning- and Deep Learning-Based Solutions for DDoS Attack Detection in SDN,” Arab. J. Sci. Eng., vol. 49, no. 3, pp. 3533–3573, Mar. 2024, doi: 10.1007/s13369- 023-08075-2Toyeer-E-Ferdoush, H. Rahman, and M. Hasan, “A convenient way to mitigate DDoS TCP SYN flood attack,” J. Discret. Math. Sci. Cryptogr., vol. 25, no. 7, pp. 2069–2077, Oct. 2022, doi: 10.1080/09720529.2022.2133246.H. Qian and L. Cai, “Improved K-means-based solution for detecting DDoS attacks in SDN,” Phys. Commun., vol. 64, p. 102318, Jun. 2024, doi: 10.1016/j.phycom.2024.102318.Y. Gao and M. Xu, “Defense Against Software-Defined Network Topology Poisoning Attacks,” Tsinghua Sci. Technol., vol. 28, no. 1, pp. 39–46, 2023, doi: 10.26599/TST.2021.9010077.S. Sunil Gowda and R. B. Dayananda, “Detection And Prevention of ARP Attack in Software Defined Networks,” in 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023, 2023. doi: 10.1109/ICCCNT56998.2023.10307123.A. T. Phu et al., “Defending SDN against packet injection attacks using deep learning,” Comput. 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Analisys of Effects Generated by Cyberattacks on an SDN Controller in a Synthetic Data Environment

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