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Artificial intelligence for the development of the aeronautical-military sector in a cybersecurity environment
Inteligencia artificial para el desarrollo del sector aeronáutico-militar en entorno de seguridad cibernética
dc.creator | Vásquez Ruiz, María Carolina | |
dc.date | 2024-12-30 | |
dc.date.accessioned | 2025-06-06T22:32:30Z | |
dc.date.available | 2025-06-06T22:32:30Z | |
dc.identifier | https://esdegrevistas.edu.co/index.php/rcit/article/view/4941 | |
dc.identifier | 10.25062/2955-0270.4941 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14205/11592 | |
dc.description | The military-aeronautical sector faces challenges in the fields of cybersecurity and operational efficiency in a complex global environment. Artificial intelligence improves the security and adaptability of aeronautical infrastructure and military air assets, but also introduces new cyber vulnerabilities. With increasingly interconnected and constantly developing systems, the surface area for cyber-attacks increases, such as network infiltrations by ransomware to air traffic control systems or execution of DDoS attacks on airport infrastructures. This research identifies the areas where AI can bring significant technical benefits, characterize its relationships with these areas and reduce the associated cyber risks, to ensure the integrity and availability of critical systems. | en-US |
dc.description | El sector aeronáutico-militar enfrenta desafíos en los campos de seguridad cibernética y eficiencia operacional en un medioambiente global complejo. La inteligencia artificial mejora la seguridad y adaptabilidad de la infraestructura aeronáutica y de los activos aéreos militares, pero también introduce nuevas vulnerabilidades cibernéticas. Con sistemas cada vez más interconectados y en constante desarrollo, incrementa la superficie de ataques cibernéticos, como infiltraciones de redes por parte de ransomware a los sistemas de control de tráfico aéreo o ejecución de ataques de DDoS en las infraestructuras aeroportuarias. Esta investigación identifica las áreas donde la IA puede traer significativos beneficios técnicos, caracterizar sus relaciones con estas áreas y reducir los riesgos cibernéticos asociados, para garantizar la integridad y disponibilidad de los sistemas críticos. | es-ES |
dc.format | application/pdf | |
dc.language | spa | |
dc.publisher | Sello Editorial ESDEG | es-ES |
dc.relation | https://esdegrevistas.edu.co/index.php/rcit/article/view/4941/5344 | |
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dc.relation | /*ref*/Bernard, D., Dorais, G., Fry, C., Gamble, E., Kanefsky, B., Kurien, J., Millar, W., Muscettola, N., Nayak, P., Pell, B., Rajan, K., Rouquette, N., Smith, B., & Williams, B. (1998). Design of the Remote Agent experiment for spacecraft autonomy. 1998 IEEE Aerospace Conference Proceedings (Cat. No.98TH8339), 2, 259-281 vol.2. https://doi.org/10.1109/AERO.1998.687914 | |
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dc.relation | /*ref*/EATM-CERT. (2021). AI as cyber-protection assistant: Aviation-related document leaks — Eurocontrol. https://n9.cl/wncgv | |
dc.relation | /*ref*/Echevarría, A. (2003). Clausewitz’s Center of Gravity: It’s Not What We Thought. Naval War College Review, 56(1), 17. | |
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dc.relation | /*ref*/Flórez, J., Vargas, J., & Reina, J. (2017). Intelligent Techniques for Identification and Tracking of Meteorological Phenomena that Could Affect Flight Safety. Ciencia y Poder Aéreo, 12(1), 24-35. https://doi.org/10.18667/cienciaypoderaereo.559 | |
dc.relation | /*ref*/Frigo, M., Silva, E. C. C. da, & Barbosa, G. (2016). Augmented Reality in Aerospace Manufacturing: A Review. Journal of Industrial and Intelligent Information. https://doi.org/10.18178/JIII.4.2.125-130 | |
dc.relation | /*ref*/García Medina, M. (2021). Desarrollo algoritmos de inteligencia artificial para predecir emisiones contaminantes en motores de encendido por compresión [Universitat Politècnica de València]. https://riunet.upv.es/handle/10251/169756 | |
dc.relation | /*ref*/Gniesko, C. (2019). Matriz del Centro de Gravedad. Military Review, Primer Trimestre 2019, 57-73. | |
dc.relation | /*ref*/Gui, G., Zhou, Z., Wang, J., Liu, F., & Sun, J. (2020). Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data. IEEE Transactions on Vehicular Technology, 69, 4817-4826. https://doi.org/10.1109/TVT.2020.2981959 | |
dc.relation | /*ref*/Harwood, S. (2022). Aviation is facing a rising wave of cyber-attacks in the wake of COVID. https://n9.cl/ol5ym | |
dc.relation | /*ref*/Heavy.AI. (2022). What is Predictive Maintenance? Definition and FAQs — Heavy.AI. https://n9.cl/6yfod | |
dc.relation | /*ref*/Leyva, R. (2023, 3 de agosto). ¿Cómo interviene la inteligencia artificial en la aviación? https://n9.cl/9gpir | |
dc.relation | /*ref*/Mertins, K., Knothe, T., & Gocev, P. (2012). Towards CPS Based Aircraft MRO. 166-173. https://doi.org/10.1007/978-3-642-32775-9_17 | |
dc.relation | /*ref*/Mojica, P., Cuéllar, S., Medina, C., & Fuerza Aérea Colombiana. (2017). Inteligencia artificial y control del espacio aéreo. Boletín Tecnológico. https://n9.cl/62gfb | |
dc.relation | /*ref*/Morales, N. (2015). Investigación exploratoria: Tipos, metodología y ejemplos. https://n9.cl/9bqza | |
dc.relation | /*ref*/Moreno, J. (2019). Aprendizaje automatizado y aplicaciones. https://n9.cl/o5vnt | |
dc.relation | /*ref*/Muscettola, N., Nayak, P., Pell, B., & Williams, B. (1998). Remote Agent: To Boldly Go Where No AI System Has Gone Before. Artif. Intell., 103, 5-47. https://doi.org/10.1016/S0004-3702(98)00068-X | |
dc.relation | /*ref*/Oroumieh, M. A. A., Malaek, S., Ashrafizaadeh, M., & Taheri, S. (2013). Aircraft design cycle time reduction using artificial intelligence. Aerospace Science and Technology, 26, 244-258. https://doi.org/10.1016/J.AST.2012.05.003 | |
dc.relation | /*ref*/Periañez, J. L. (2023, 26 de octubre). Inteligencia artificial en aeronáutica. AERTEC. https://n9.cl/qyiwe6 | |
dc.relation | /*ref*/Review, E. (2020, 17 de febrero). Indra Revolutionizes Air Traffic Control with Artificial Intelligence Remote Tower. Eurasia Review. https://n9.cl/0wsv4 | |
dc.relation | /*ref*/Rodríguez, P. (2021). Inteligencia artificial para la administración de los Reglamentos Aeronáuticos De Colombia (RAC). Revista Estrategia Organizacional, 10(1). https://doi.org/10.22490/25392786.4551 | |
dc.relation | /*ref*/Romero, M. (2021). La Industria 4.0 en el sector aeronáutico. Universidad de Sevilla. https://n9.cl/9ynp1m | |
dc.relation | /*ref*/Safecore. (2023, 27 de marzo). Ciberseguridad en el sector Aeroespacial: Escenario, riesgos y retos de futuro — Safecore. https://n9.cl/pj6jd | |
dc.relation | /*ref*/Santos, J., Almeida, G., Torres, D., & Apolinarie, K. (2024). The Use of Artificial Intelligence as a National Defense Strategy. Journal of Engineering Research, 4(6). https://doi.org/10.22533/at.ed.317462423028 | |
dc.relation | /*ref*/Sherwood, R., Chien, S. A., Tran, D., Cichy, B., Castaño, R., Davies, A., & Rabideau, G. (2004). Preliminary results of the Autonomous Sciencecraft Experiment. 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720), 1(192). https://doi.org/10.1109/AERO.2004.1367604 | |
dc.relation | /*ref*/Software Gephi. (2022). Gephi-The Open Graph Viz Platform (Version Gephi 0.10.1 202301172018) [NetBeans IDE y NetBeans Platform; Java 11.0.17; OpenJDK 64-Bit Server VM 11.0.17+8]. Gephi. https://gephi.org/ | |
dc.relation | /*ref*/Sosa, C. (2023). Diseño de programa de instrucción y entrenamiento en toma de decisiones aeronáuticas para pilotos de helicópteros livianos de la Fuerza Aérea Colombiana [Escuela de Postgrados Fuerza Aérea Colombiana]. https://n9.cl/9jxn3 | |
dc.relation | /*ref*/Swett, B. A., Hahn, E. N., & Llorens, A. J. (2021). Designing robots for the battlefield: State of the art. Robotics, AI, and humanity: Science, ethics, and policy, 131-146. | |
dc.relation | /*ref*/Takemura, T. (2023). New Challenge in Predictive Maintenance Analysis for Aircraft. 4th Asia Pacific Conference of the Prognostics and Health Management, OS04-12, 3. https://doi.org/10.36001/phmap.2023.v4i1.3728 | |
dc.relation | /*ref*/Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. ukasz, & Polosukhin, I. (2017). Attention is All you Need. Advances in Neural Information Processing Systems, 30. https://n9.cl/s4wx2 | |
dc.relation | /*ref*/Vora, J., Nair, S., Gramopadhye, A., Duchowski, A., Melloy, B., & Kanki, B. (2002). Using virtual reality technology for aircraft visual inspection training: Presence and comparison studies. Applied ergonomics, 33 (6), 559-570. https://doi.org/10.1016/S0003-6870(02)00039-X | |
dc.relation | /*ref*/Xie, Y., Pongsakornsathien, N., Gardi, A., & Sabatini, R. (2021). Explanation of Machine-Learning Solutions in Air-Traffic Management. Aerospace, 8(224), 25. https://doi.org/10.3390/aerospace8080224 | |
dc.relation | /*ref*/American Express. (2021). La evolución de la aeronáutica con la industria 4.0. Business Class: Trends & Insights — American Express. https://n9.cl/xjwyi | |
dc.relation | /*ref*/Anaya, R. (2017). ¿Cómo utilizará el sector aéreo la inteligencia artificial? https://n9.cl/wza9ck | |
dc.relation | /*ref*/András, M. (2021). Command and Control of Multi-domain Operations. Hadtudomány, 31(E-szám), 12-28. https://doi.org/10.17047/HADTUD.2021.31.E.12 | |
dc.relation | /*ref*/Arghire, I. (2022, 15 de marzo). FBI: 649 Ransomware Attacks Reported on Critical Infrastructure Organizations in 2021. Security Week. https://n9.cl/u1hqs | |
dc.relation | /*ref*/Atkins, E., & Bradley, J. M. (2013). Aerospace Cyber-Physical Systems Education. https://doi.org/10.2514/6.2013-4809 | |
dc.relation | /*ref*/Bernard, D., Dorais, G., Fry, C., Gamble, E., Kanefsky, B., Kurien, J., Millar, W., Muscettola, N., Nayak, P., Pell, B., Rajan, K., Rouquette, N., Smith, B., & Williams, B. (1998). Design of the Remote Agent experiment for spacecraft autonomy. 1998 IEEE Aerospace Conference Proceedings (Cat. No.98TH8339), 2, 259-281 vol.2. https://doi.org/10.1109/AERO.1998.687914 | |
dc.relation | /*ref*/Castet, N. (2019). Using AI and Deep Learning for automatic defect detection. | |
dc.relation | /*ref*/Castillo, E., Gutiérrez, J. M., & Hadi, A. S. (1997). Sistemas expertos y modelos de redes probabilísticas. Academia de Ingeniería. https://n9.cl/kd85b | |
dc.relation | /*ref*/CERT SysDream. (2023). Cyber Threat Landscape in the Aviation industry in 2023—SysDream. https://n9.cl/fxisk | |
dc.relation | /*ref*/Chien, S., Doyle, R., Davies, A. G., Jonsson, A., & Lorenz, R. (2006). The Future of AI in Space. IEEE Intelligent Systems, 21(4), 64-69. https://doi.org/10.1109/MIS.2006.79 | |
dc.relation | /*ref*/Comisión Europea. (2018). Enhance Aircraft Performance and Optimisation through utilisation of Artificial Intelligence — PERF-AI Project — Fact Sheet — H2020. CORDIS — European Commission. https://n9.cl/hqzga | |
dc.relation | /*ref*/Community Research and Development Information Service. (2022). Introducción de una automatización inteligente y fiable en el sector de la aviación europeo. CORDIS - European Commission. https://n9.cl/y4v73 | |
dc.relation | /*ref*/Contreras, H. (2012). Teoría de la Computación para Ingeniería de Sistemas: Un.dotData. (2023). Case Study: Japan Airlines Uses Predictive Analytics to Strive for Zero Delays. dotData. https://n9.cl/v8til | |
dc.relation | /*ref*/Doukidis, G. (1987). An Anthology on the Homology of Simulation with Artificial Intelligence. Journal of the Operational Research Society, 38, 701-712. https://doi.org/10.1057/JORS.1987.119 | |
dc.relation | /*ref*/Dufrene, W. R. (2004). Approach for autonomous control of unmanned aerial vehicle using intelligent agents for knowledge creation. The 23rd Digital Avionics Systems Conference (IEEE Cat. No. 04CH37576), 2, 12. E. 3-12.1. https://doi.org/10.1109/DASC.2004.1390846 | |
dc.relation | /*ref*/Dunford, R., Su, Q., & Tamang, E. (2021). The Pareto Principle. The Race. https://doi.org/10.4324/9780429333705-66 | |
dc.relation | /*ref*/EATM-CERT. (2021). AI as cyber-protection assistant: Aviation-related document leaks — Eurocontrol. https://n9.cl/wncgv | |
dc.relation | /*ref*/Echevarría, A. (2003). Clausewitz’s Center of Gravity: It’s Not What We Thought. Naval War College Review, 56(1), 17. | |
dc.relation | /*ref*/Eikmeier, D. (2018). El centro de gravedad: ¿Aún relevante después de todos estos años? Military Review, Primer Trimestre. | |
dc.relation | /*ref*/Eurocontrol (Director). (2021, 15 de junio). FLY AI #6—AI for cyber and cyber for AI [Video recording]. https://www.youtube.com/watch?v=6pNN5vK2PmE | |
dc.relation | /*ref*/Europair. (2024, 29 de febrero). El poder de la inteligencia artificial en la aviación. https://n9.cl/svow6 | |
dc.relation | /*ref*/FlightSafety. (2019). FlightSafety Introduces FlightSmart, A New Integrated Pilot Performance Evaluation and Training Tool Developed in Conjunction With IBM. FlightSafety International Media Center. https://n9.cl/h1e7h | |
dc.relation | /*ref*/Flórez, J. (2013). Tracking y Multitracking Radar. Ciencia y Poder Aéreo, 8(1), Article 1. https://doi.org/10.18667/cienciaypoderaereo.9 | |
dc.relation | /*ref*/Flórez, J., Vargas, J., & Reina, J. (2017). Intelligent Techniques for Identification and Tracking of Meteorological Phenomena that Could Affect Flight Safety. Ciencia y Poder Aéreo, 12(1), 24-35. https://doi.org/10.18667/cienciaypoderaereo.559 | |
dc.relation | /*ref*/Frigo, M., Silva, E. C. C. da, & Barbosa, G. (2016). Augmented Reality in Aerospace Manufacturing: A Review. Journal of Industrial and Intelligent Information. https://doi.org/10.18178/JIII.4.2.125-130 | |
dc.relation | /*ref*/García Medina, M. (2021). Desarrollo algoritmos de inteligencia artificial para predecir emisiones contaminantes en motores de encendido por compresión [Universitat Politècnica de València]. https://riunet.upv.es/handle/10251/169756 | |
dc.relation | /*ref*/Gniesko, C. (2019). Matriz del Centro de Gravedad. Military Review, Primer Trimestre 2019, 57-73. | |
dc.relation | /*ref*/Gui, G., Zhou, Z., Wang, J., Liu, F., & Sun, J. (2020). Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data. IEEE Transactions on Vehicular Technology, 69, 4817-4826. https://doi.org/10.1109/TVT.2020.2981959 | |
dc.relation | /*ref*/Harwood, S. (2022). Aviation is facing a rising wave of cyber-attacks in the wake of COVID. https://n9.cl/ol5ym | |
dc.relation | /*ref*/Heavy.AI. (2022). What is Predictive Maintenance? Definition and FAQs — Heavy.AI. https://n9.cl/6yfod | |
dc.relation | /*ref*/Leyva, R. (2023, 3 de agosto). ¿Cómo interviene la inteligencia artificial en la aviación? https://n9.cl/9gpir | |
dc.relation | /*ref*/Mertins, K., Knothe, T., & Gocev, P. (2012). Towards CPS Based Aircraft MRO. 166-173. https://doi.org/10.1007/978-3-642-32775-9_17 | |
dc.relation | /*ref*/Mojica, P., Cuéllar, S., Medina, C., & Fuerza Aérea Colombiana. (2017). Inteligencia artificial y control del espacio aéreo. Boletín Tecnológico. https://n9.cl/62gfb | |
dc.relation | /*ref*/Morales, N. (2015). Investigación exploratoria: Tipos, metodología y ejemplos. https://n9.cl/9bqza | |
dc.relation | /*ref*/Moreno, J. (2019). Aprendizaje automatizado y aplicaciones. https://n9.cl/o5vnt | |
dc.relation | /*ref*/Muscettola, N., Nayak, P., Pell, B., & Williams, B. (1998). Remote Agent: To Boldly Go Where No AI System Has Gone Before. Artif. Intell., 103, 5-47. https://doi.org/10.1016/S0004-3702(98)00068-X | |
dc.relation | /*ref*/Oroumieh, M. A. A., Malaek, S., Ashrafizaadeh, M., & Taheri, S. (2013). Aircraft design cycle time reduction using artificial intelligence. Aerospace Science and Technology, 26, 244-258. https://doi.org/10.1016/J.AST.2012.05.003 | |
dc.relation | /*ref*/Periañez, J. L. (2023, 26 de octubre). Inteligencia artificial en aeronáutica. AERTEC. https://n9.cl/qyiwe6 | |
dc.relation | /*ref*/Review, E. (2020, 17 de febrero). Indra Revolutionizes Air Traffic Control with Artificial Intelligence Remote Tower. Eurasia Review. https://n9.cl/0wsv4 | |
dc.relation | /*ref*/Rodríguez, P. (2021). Inteligencia artificial para la administración de los Reglamentos Aeronáuticos De Colombia (RAC). Revista Estrategia Organizacional, 10(1). https://doi.org/10.22490/25392786.4551 | |
dc.relation | /*ref*/Romero, M. (2021). La Industria 4.0 en el sector aeronáutico. Universidad de Sevilla. https://n9.cl/9ynp1m | |
dc.relation | /*ref*/Safecore. (2023, 27 de marzo). Ciberseguridad en el sector Aeroespacial: Escenario, riesgos y retos de futuro — Safecore. https://n9.cl/pj6jd | |
dc.relation | /*ref*/Santos, J., Almeida, G., Torres, D., & Apolinarie, K. (2024). The Use of Artificial Intelligence as a National Defense Strategy. Journal of Engineering Research, 4(6). https://doi.org/10.22533/at.ed.317462423028 | |
dc.relation | /*ref*/Sherwood, R., Chien, S. A., Tran, D., Cichy, B., Castaño, R., Davies, A., & Rabideau, G. (2004). Preliminary results of the Autonomous Sciencecraft Experiment. 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720), 1(192). https://doi.org/10.1109/AERO.2004.1367604 | |
dc.relation | /*ref*/Software Gephi. (2022). Gephi-The Open Graph Viz Platform (Version Gephi 0.10.1 202301172018) [NetBeans IDE y NetBeans Platform; Java 11.0.17; OpenJDK 64-Bit Server VM 11.0.17+8]. Gephi. https://gephi.org/ | |
dc.relation | /*ref*/Sosa, C. (2023). Diseño de programa de instrucción y entrenamiento en toma de decisiones aeronáuticas para pilotos de helicópteros livianos de la Fuerza Aérea Colombiana [Escuela de Postgrados Fuerza Aérea Colombiana]. https://n9.cl/9jxn3 | |
dc.relation | /*ref*/Swett, B. A., Hahn, E. N., & Llorens, A. J. (2021). Designing robots for the battlefield: State of the art. Robotics, AI, and humanity: Science, ethics, and policy, 131-146. | |
dc.relation | /*ref*/Takemura, T. (2023). New Challenge in Predictive Maintenance Analysis for Aircraft. 4th Asia Pacific Conference of the Prognostics and Health Management, OS04-12, 3. https://doi.org/10.36001/phmap.2023.v4i1.3728 | |
dc.relation | /*ref*/Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. ukasz, & Polosukhin, I. (2017). Attention is All you Need. Advances in Neural Information Processing Systems, 30. https://n9.cl/s4wx2 | |
dc.relation | /*ref*/Vora, J., Nair, S., Gramopadhye, A., Duchowski, A., Melloy, B., & Kanki, B. (2002). Using virtual reality technology for aircraft visual inspection training: Presence and comparison studies. Applied ergonomics, 33 (6), 559-570. https://doi.org/10.1016/S0003-6870(02)00039-X | |
dc.relation | /*ref*/Xie, Y., Pongsakornsathien, N., Gardi, A., & Sabatini, R. (2021). Explanation of Machine-Learning Solutions in Air-Traffic Management. Aerospace, 8(224), 25. https://doi.org/10.3390/aerospace8080224 | |
dc.rights | Derechos de autor 2024 Revista Ciberespacio, Tecnología e Innovación | es-ES |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0 | es-ES |
dc.source | Revista Ciberespacio, Tecnología e Innovación; Vol. 3 Núm. 6 (2024): La ciberseguridad como pilar para la protección de infraestructuras estratégicas y la seguridad nacional; 151-174 | es-ES |
dc.source | 3028-3310 | |
dc.source | 2955-0270 | |
dc.subject | aeronáutica militar | es-ES |
dc.subject | algoritmos | es-ES |
dc.subject | amenazas cibernéticas | es-ES |
dc.subject | aprendizaje automático | es-ES |
dc.subject | aprendizaje profundo | es-ES |
dc.subject | tecnología militar | es-ES |
dc.subject | military aeronautics | en-US |
dc.subject | algorithms | en-US |
dc.subject | cyber threats | en-US |
dc.subject | machine learning | en-US |
dc.subject | deep learning | en-US |
dc.subject | military Technology | en-US |
dc.title | Artificial intelligence for the development of the aeronautical-military sector in a cybersecurity environment | en-US |
dc.title | Inteligencia artificial para el desarrollo del sector aeronáutico-militar en entorno de seguridad cibernética | es-ES |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:eu-repo/semantics/publishedVersion |
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