Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones
Security in smart cities is a challenging issue in urban environments as they depend upon interconnected technologies and data for effective services. To address security challenges, smart cities implement robust cybersecurity measures, including network monitoring, encryption, and intrusion detecti...
- Autores:
-
Alsariera, Yazan A.
Awwad, Waleed Fayez
Algarni, Abeer D.
Elmannai, Hela
Gamarra, Margarita
Escorcia Gutierrez, José
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13818
- Acceso en línea:
- https://hdl.handle.net/11323/13818
https://repositorio.cuc.edu.co/
- Palabra clave:
- Deep learning
Dwarf mongoose optimization algorithm
Security
Smart cities attacks
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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|
dc.title.eng.fl_str_mv |
Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones |
title |
Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones |
spellingShingle |
Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones Deep learning Dwarf mongoose optimization algorithm Security Smart cities attacks |
title_short |
Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones |
title_full |
Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones |
title_fullStr |
Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones |
title_full_unstemmed |
Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones |
title_sort |
Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones |
dc.creator.fl_str_mv |
Alsariera, Yazan A. Awwad, Waleed Fayez Algarni, Abeer D. Elmannai, Hela Gamarra, Margarita Escorcia Gutierrez, José |
dc.contributor.author.none.fl_str_mv |
Alsariera, Yazan A. Awwad, Waleed Fayez Algarni, Abeer D. Elmannai, Hela Gamarra, Margarita Escorcia Gutierrez, José |
dc.subject.proposal.eng.fl_str_mv |
Deep learning Dwarf mongoose optimization algorithm Security Smart cities attacks |
topic |
Deep learning Dwarf mongoose optimization algorithm Security Smart cities attacks |
description |
Security in smart cities is a challenging issue in urban environments as they depend upon interconnected technologies and data for effective services. To address security challenges, smart cities implement robust cybersecurity measures, including network monitoring, encryption, and intrusion detection systems. Detecting and mitigating possible security risks in drone network B5G is a crucial aspect of ensuring reliable and safe drone operation. It is necessary to establish sophisticated and robust attack detection techniques to defend against security threats as the use of drones becomes increasingly widespread and their applications diversify. This is due to the lack of privacy and security consideration in the drone's system, including an inadequate computation capability and unsecured wireless channels to perform advanced cryptographic algorithms. Intrusion detection systems (IDS) and anomaly detection systems can identify suspicious activities and monitor network traffic, such as anomalous communication patterns or unauthorized access attempts. Therefore, the study presents an enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection (EDMOA-DLAD) in Networks B5G for the purpose of Drones technique. The presented EDMOA-DLAD technique aims to recognize the attacks and classifies them on the drone network B5G. Primarily, the EDMOA-DLAD technique designs a feature selection (FS) approach using EDMOA. To detect attacks, the EDMOA-DLAD technique uses a deep variational autoencoder (DVAE) classifier. Finally, the EDMOA-DLAD technique applies the beetle antenna search (BAS) technique for the optimum hyperparameter part of DVAE model. The outcome of EDMOA-DLAD approach can be verified on benchmark datasets. A wide range of simulations inferred that the EDMOA-DLAD method obtains enhanced performance of 99.79% over other classification techniques. |
publishDate |
2024 |
dc.date.accessioned.none.fl_str_mv |
2024-11-25T17:51:23Z |
dc.date.available.none.fl_str_mv |
2024-11-25T17:51:23Z |
dc.date.issued.none.fl_str_mv |
2024-03-14 |
dc.type.none.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Yazan A. Alsariera, Waleed Fayez Awwad, Abeer D. Algarni, Hela Elmannai, Margarita Gamarra, José Escorcia-Gutierrez, Enhanced Dwarf Mongoose optimization algorithm with deep learning-based attack detection for drones, Alexandria Engineering Journal, Volume 93, 2024, Pages 59-66, ISSN 1110-0168, https://doi.org/10.1016/j.aej.2024.02.048. |
dc.identifier.issn.none.fl_str_mv |
1110-0168 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/13818 |
dc.identifier.doi.none.fl_str_mv |
10.1016/j.aej.2024.02.048 |
dc.identifier.eissn.none.fl_str_mv |
2090-2670 |
dc.identifier.instname.none.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.none.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.none.fl_str_mv |
https://repositorio.cuc.edu.co/ |
identifier_str_mv |
Yazan A. Alsariera, Waleed Fayez Awwad, Abeer D. Algarni, Hela Elmannai, Margarita Gamarra, José Escorcia-Gutierrez, Enhanced Dwarf Mongoose optimization algorithm with deep learning-based attack detection for drones, Alexandria Engineering Journal, Volume 93, 2024, Pages 59-66, ISSN 1110-0168, https://doi.org/10.1016/j.aej.2024.02.048. 1110-0168 10.1016/j.aej.2024.02.048 2090-2670 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
url |
https://hdl.handle.net/11323/13818 https://repositorio.cuc.edu.co/ |
dc.language.iso.none.fl_str_mv |
eng |
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eng |
dc.relation.ispartofjournal.none.fl_str_mv |
Alexandria Engineering Journal (AEJ) |
dc.relation.references.none.fl_str_mv |
V. Sadhu, K. Anjum, D. Pompili, On-board deep-learning-based unmanned aerial vehicle fault cause detection and classification via FPGAs, IEEE Trans. Robot. (2023). V. Sadhu, K. Anjum, D. Pompili, On-board deep-learning-based unmanned aerial vehicle fault cause detection and classification via FPGAs, IEEE Trans. Robot. (2023). K. Yu, L. Tan, S. Mumtaz, S. Al-Rubaye, A. Al-Dulaimi, A.K. Bashir, F.A. Khan, Securing critical infrastructures: deep-learning-based threat detection in IIoT, IEEE Commun. Mag. 59 (10) (2021) 76–82. T.T. Khoei, G. Aissou, K. Al Shamaileh, V.K. Devabhaktuni, N. Kaabouch, Supervised deep learning models for detecting GPS spoofing attacks on unmanned aerial vehicles. in: Proceedings of the 2023 IEEE International Conference on Electro Information Technology (eIT), IEEE, 2023, pp. 340–346. R.A. Agyapong, M. Nabil, A.R. Nuhu, M.I. Rasul, A. Homaifar, Efficient detection of GPS spoofing attacks on unmanned aerial vehicles using deep learning. in: Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2021, pp. 01–08. W. Zhai, L. Liu, Y. Ding, S. Sun, Y. Gu, ETD: an efficient time delay attack detection framework for UAV networks, IEEE Trans. Inf. Forensics Secur. (2023). J. Kim, D. Lee, Y. Kim, H. Shin, Y. Heo, Y. Wang, E.T. Matson, Deep learning based malicious drone detection using acoustic and image data. in: Proceedings of the 2022 Sixth IEEE International Conference on Robotic Computing (IRC), IEEE, 2022, pp. 91–92. Y. Dang, C. Benzaïd, B. Yang, T. Taleb, Deep learning for GPS spoofing detection in cellular-enabled UAV systems. in: Proceedings of the 2021 International Conference on Networking and Network Applications (NaNA), IEEE, 2021, pp. 501–506. Ashraf, S.N., Manickam, S., Zia, S.S., Abro, A.A., Obaidat, M., Uddin, M., Abdelhaq, M. and Alsaqour, R., 2023. IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones. A. Khazraei, H. Meng, M. Pajic, Stealthy perception-based attacks on unmanned aerial vehicles, arXiv Prepr. arXiv:2303. 02112 (2023). J. Escorcia-Gutierrez, M. Gamarra, E. Leal, N. Madera, C. Soto, R.F. Mansour, M. Alharbi, A. Alkhayyat, D. Gupta, Sea turtle foraging algorithm with hybrid deep learning-based intrusion detection for the internet of drones environment, Comput. Electr. Eng. 108 (2023) 108704. Q. Abu Al-Haija, A. Al Badawi, High-performance intrusion detection system for networked UAVs via deep learning, Neural Comput. Appl. 34 (13) (2022) 10885–10900. R.A. Ramadan, A.H. Emara, M. Al-Sarem, M. Elhamahmy, Internet of drones intrusion detection using deep learning, Electronics 10 (21) (2021) 2633 Y. Sun, M. Yu, L. Wang, T. Li, M. Dong, A deep-learning-based gps signal spoofing detection method for small UAVs, Drones 7 (6) (2023) 370. J. Tao, T. Han, R. Li, Deep-reinforcement-learning-based intrusion detection in aerial computing networks, IEEE Netw. 35 (4) (2021) 66–72. J. Viana, H. Farkhari, L.M. Campos, P. Sebastiao, ˜ K. Koutlia, S. Lag´en, L. Bernardo, R. Dinis, A convolutional attention based deep learning solution for 5G UAV network attack recognition over fading channels and interference. in: Proceedings of the 2022 IEEE Ninety Sixth Vehicular Technology Conference (VTC2022-Fall), IEEE, 2022, pp. 1–5 A. Heidari, N.J. Navimipour, M. Unal, A secure intrusion detection platform using blockchain and radial basis function neural networks for internet of drones, IEEE Internet Things J. (2023). Y. Dang, C. Benzaïd, B. Yang, T. Taleb, Y. Shen, Deep-ensemble-learning-based GPS spoofing detection for cellular-connected UAVs, IEEE Internet Things J. 9 (24) (2022) 25068–25085 G. Moustafa, A.M. El-Rifaie, I.H. Smaili, A. Ginidi, A.M. Shaheen, A.F. Youssef, M. A. Tolba, An enhanced dwarf mongoose optimization algorithm for solving engineering problems, Mathematics 11 (15) (2023) 3297. M. Dai, D. Zheng, R. Na, S. Wang, S. Zhang, EEG classification of motor imagery using a novel deep learning framework, Sensors 19 (3) (2019) 551. Z. Yu, J. Yuan, Y. Li, C. Yuan, S. Deng, A path planning algorithm for mobile robot based on water flow potential field method and beetle antennae search algorithm, Comput. Electr. Eng. 109 (2023) 108730. https://www.unb.ca/cic/datasets/nsl.html F.N. Al-Wesabi, F. Alrowais, J.S. Alzahrani, R. Marzouk, M. Al Duhayyim, D. Gupta, Oppositional poor and rich optimization with deep learning enabled secure internet of drone communication system, Comput. Electr. Eng. 104 (2022) 108368. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2024 The Author(s).https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Alsariera, Yazan A.Awwad, Waleed FayezAlgarni, Abeer D.Elmannai, HelaGamarra, MargaritaEscorcia Gutierrez, José2024-11-25T17:51:23Z2024-11-25T17:51:23Z2024-03-14Yazan A. Alsariera, Waleed Fayez Awwad, Abeer D. Algarni, Hela Elmannai, Margarita Gamarra, José Escorcia-Gutierrez, Enhanced Dwarf Mongoose optimization algorithm with deep learning-based attack detection for drones, Alexandria Engineering Journal, Volume 93, 2024, Pages 59-66, ISSN 1110-0168, https://doi.org/10.1016/j.aej.2024.02.048.1110-0168https://hdl.handle.net/11323/1381810.1016/j.aej.2024.02.0482090-2670Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Security in smart cities is a challenging issue in urban environments as they depend upon interconnected technologies and data for effective services. To address security challenges, smart cities implement robust cybersecurity measures, including network monitoring, encryption, and intrusion detection systems. Detecting and mitigating possible security risks in drone network B5G is a crucial aspect of ensuring reliable and safe drone operation. It is necessary to establish sophisticated and robust attack detection techniques to defend against security threats as the use of drones becomes increasingly widespread and their applications diversify. This is due to the lack of privacy and security consideration in the drone's system, including an inadequate computation capability and unsecured wireless channels to perform advanced cryptographic algorithms. Intrusion detection systems (IDS) and anomaly detection systems can identify suspicious activities and monitor network traffic, such as anomalous communication patterns or unauthorized access attempts. Therefore, the study presents an enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection (EDMOA-DLAD) in Networks B5G for the purpose of Drones technique. The presented EDMOA-DLAD technique aims to recognize the attacks and classifies them on the drone network B5G. Primarily, the EDMOA-DLAD technique designs a feature selection (FS) approach using EDMOA. To detect attacks, the EDMOA-DLAD technique uses a deep variational autoencoder (DVAE) classifier. Finally, the EDMOA-DLAD technique applies the beetle antenna search (BAS) technique for the optimum hyperparameter part of DVAE model. The outcome of EDMOA-DLAD approach can be verified on benchmark datasets. A wide range of simulations inferred that the EDMOA-DLAD method obtains enhanced performance of 99.79% over other classification techniques.8 páginasapplication/pdfengElsevier B.V.Netherlandshttps://www.sciencedirect.com/science/article/pii/S1110016824001881?via%3DihubEnhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for dronesArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Alexandria Engineering Journal (AEJ)V. Sadhu, K. Anjum, D. Pompili, On-board deep-learning-based unmanned aerial vehicle fault cause detection and classification via FPGAs, IEEE Trans. Robot. (2023).V. Sadhu, K. Anjum, D. Pompili, On-board deep-learning-based unmanned aerial vehicle fault cause detection and classification via FPGAs, IEEE Trans. Robot. (2023).K. Yu, L. Tan, S. Mumtaz, S. Al-Rubaye, A. Al-Dulaimi, A.K. Bashir, F.A. Khan, Securing critical infrastructures: deep-learning-based threat detection in IIoT, IEEE Commun. Mag. 59 (10) (2021) 76–82.T.T. Khoei, G. Aissou, K. Al Shamaileh, V.K. Devabhaktuni, N. Kaabouch, Supervised deep learning models for detecting GPS spoofing attacks on unmanned aerial vehicles. in: Proceedings of the 2023 IEEE International Conference on Electro Information Technology (eIT), IEEE, 2023, pp. 340–346.R.A. Agyapong, M. Nabil, A.R. Nuhu, M.I. Rasul, A. Homaifar, Efficient detection of GPS spoofing attacks on unmanned aerial vehicles using deep learning. in: Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2021, pp. 01–08.W. Zhai, L. Liu, Y. Ding, S. Sun, Y. Gu, ETD: an efficient time delay attack detection framework for UAV networks, IEEE Trans. Inf. Forensics Secur. (2023).J. Kim, D. Lee, Y. Kim, H. Shin, Y. Heo, Y. Wang, E.T. Matson, Deep learning based malicious drone detection using acoustic and image data. in: Proceedings of the 2022 Sixth IEEE International Conference on Robotic Computing (IRC), IEEE, 2022, pp. 91–92.Y. Dang, C. Benzaïd, B. Yang, T. Taleb, Deep learning for GPS spoofing detection in cellular-enabled UAV systems. in: Proceedings of the 2021 International Conference on Networking and Network Applications (NaNA), IEEE, 2021, pp. 501–506.Ashraf, S.N., Manickam, S., Zia, S.S., Abro, A.A., Obaidat, M., Uddin, M., Abdelhaq, M. and Alsaqour, R., 2023. IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones.A. Khazraei, H. Meng, M. Pajic, Stealthy perception-based attacks on unmanned aerial vehicles, arXiv Prepr. arXiv:2303. 02112 (2023).J. Escorcia-Gutierrez, M. Gamarra, E. Leal, N. Madera, C. Soto, R.F. Mansour, M. Alharbi, A. Alkhayyat, D. Gupta, Sea turtle foraging algorithm with hybrid deep learning-based intrusion detection for the internet of drones environment, Comput. Electr. Eng. 108 (2023) 108704.Q. Abu Al-Haija, A. Al Badawi, High-performance intrusion detection system for networked UAVs via deep learning, Neural Comput. Appl. 34 (13) (2022) 10885–10900.R.A. Ramadan, A.H. Emara, M. Al-Sarem, M. Elhamahmy, Internet of drones intrusion detection using deep learning, Electronics 10 (21) (2021) 2633Y. Sun, M. Yu, L. Wang, T. Li, M. Dong, A deep-learning-based gps signal spoofing detection method for small UAVs, Drones 7 (6) (2023) 370.J. Tao, T. Han, R. Li, Deep-reinforcement-learning-based intrusion detection in aerial computing networks, IEEE Netw. 35 (4) (2021) 66–72.J. Viana, H. Farkhari, L.M. Campos, P. Sebastiao, ˜ K. Koutlia, S. Lag´en, L. Bernardo, R. Dinis, A convolutional attention based deep learning solution for 5G UAV network attack recognition over fading channels and interference. in: Proceedings of the 2022 IEEE Ninety Sixth Vehicular Technology Conference (VTC2022-Fall), IEEE, 2022, pp. 1–5A. Heidari, N.J. Navimipour, M. Unal, A secure intrusion detection platform using blockchain and radial basis function neural networks for internet of drones, IEEE Internet Things J. (2023).Y. Dang, C. Benzaïd, B. Yang, T. Taleb, Y. Shen, Deep-ensemble-learning-based GPS spoofing detection for cellular-connected UAVs, IEEE Internet Things J. 9 (24) (2022) 25068–25085G. Moustafa, A.M. El-Rifaie, I.H. Smaili, A. Ginidi, A.M. Shaheen, A.F. Youssef, M. A. Tolba, An enhanced dwarf mongoose optimization algorithm for solving engineering problems, Mathematics 11 (15) (2023) 3297.M. Dai, D. Zheng, R. Na, S. Wang, S. Zhang, EEG classification of motor imagery using a novel deep learning framework, Sensors 19 (3) (2019) 551.Z. Yu, J. Yuan, Y. Li, C. Yuan, S. Deng, A path planning algorithm for mobile robot based on water flow potential field method and beetle antennae search algorithm, Comput. Electr. Eng. 109 (2023) 108730.https://www.unb.ca/cic/datasets/nsl.htmlF.N. Al-Wesabi, F. Alrowais, J.S. Alzahrani, R. Marzouk, M. Al Duhayyim, D. Gupta, Oppositional poor and rich optimization with deep learning enabled secure internet of drone communication system, Comput. Electr. Eng. 104 (2022) 108368.665993Deep learningDwarf mongoose optimization algorithmSecuritySmart cities attacksPublicationORIGINALEnhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones..pdfEnhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones..pdfapplication/pdf6309908https://repositorio.cuc.edu.co/bitstreams/824440ad-8ff7-41df-9445-578b87d21b58/downloadf9ba4caf1cd957e9ffd573b2bbf5e33cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/019780b0-0e11-409d-bcf1-8eb5c977187e/download73a5432e0b76442b22b026844140d683MD52TEXTEnhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones..pdf.txtEnhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones..pdf.txtExtracted 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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