Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma
Los continuos avances en la tecnología, destinados a satisfacer las demandas cambiantes de la sociedad actual, han sumergido a la humanidad en un entorno cada vez más digitalizado. Este fenómeno se traduce en un constante aumento de dispositivos conectados a través de internet, generando una masiva...
- Autores:
-
Bedoya Ocampo, Luis Miguel
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- spa
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/55992
- Acceso en línea:
- https://hdl.handle.net/20.500.12494/55992
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales
I.A
Seguridad
Directrices
Prisma
A.I
Security
Guidelines
Prism
- Rights
- closedAccess
- License
- https://creativecommons.org/licenses/by-nc-nd/4.0/
| id |
COOPER2_244688264c9aa8e34d614c50e5a270f4 |
|---|---|
| oai_identifier_str |
oai:repository.ucc.edu.co:20.500.12494/55992 |
| network_acronym_str |
COOPER2 |
| network_name_str |
Repositorio UCC |
| repository_id_str |
|
| dc.title.none.fl_str_mv |
Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma |
| title |
Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma |
| spellingShingle |
Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma 000 - Ciencias de la computación, información y obras generales I.A Seguridad Directrices Prisma A.I Security Guidelines Prism |
| title_short |
Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma |
| title_full |
Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma |
| title_fullStr |
Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma |
| title_full_unstemmed |
Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma |
| title_sort |
Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma |
| dc.creator.fl_str_mv |
Bedoya Ocampo, Luis Miguel |
| dc.contributor.advisor.none.fl_str_mv |
Cano Beltrán, Jhon Haide |
| dc.contributor.author.none.fl_str_mv |
Bedoya Ocampo, Luis Miguel |
| dc.subject.ddc.none.fl_str_mv |
000 - Ciencias de la computación, información y obras generales |
| topic |
000 - Ciencias de la computación, información y obras generales I.A Seguridad Directrices Prisma A.I Security Guidelines Prism |
| dc.subject.proposal.spa.fl_str_mv |
I.A Seguridad Directrices Prisma |
| dc.subject.proposal.eng.fl_str_mv |
A.I Security Guidelines Prism |
| description |
Los continuos avances en la tecnología, destinados a satisfacer las demandas cambiantes de la sociedad actual, han sumergido a la humanidad en un entorno cada vez más digitalizado. Este fenómeno se traduce en un constante aumento de dispositivos conectados a través de internet, generando una masiva transferencia de información y dando origen al Internet de las Cosas (IdC). Ante este panorama, es crucial que el IdC preste especial atención a posibles accesos no autorizados o manipulaciones de información sensible presentes en el flujo de datos de sus redes. En este contexto, la Inteligencia Artificial (IA) emerge como un valioso aliado tecnológico en cuestiones de seguridad y privacidad en las redes. Su papel fundamental radica en garantizar la confidencialidad de la información. Este trabajo se centrará en explorar la contribución de la inteligencia artificial para asegurar la seguridad y privacidad en entornos de red. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-06-18T21:40:29Z |
| dc.date.available.none.fl_str_mv |
2024-06-18T21:40:29Z |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TP |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_7a1f |
| status_str |
acceptedVersion |
| dc.identifier.citation.none.fl_str_mv |
Bedoya Campo, L. M. (2024). Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma [Tesis de pregrado, Universidad Cooperativa de Colombia] Repositorio Institucional Universidad Cooperativa de Colombia. https://hdl.handle.net/20.500.12494/55992 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12494/55992 |
| identifier_str_mv |
Bedoya Campo, L. M. (2024). Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma [Tesis de pregrado, Universidad Cooperativa de Colombia] Repositorio Institucional Universidad Cooperativa de Colombia. https://hdl.handle.net/20.500.12494/55992 |
| url |
https://hdl.handle.net/20.500.12494/55992 |
| dc.language.iso.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.none.fl_str_mv |
A. A. M. Sharadqh, H. A. M. Hatamleh, A. M. A. Alnaser, S. S. Saloum and T. A. Alawneh, "Hybrid Chain: Blockchain Enabled Framework for Bi-Level Intrusion Detection and Graph-Based Mitigation for Security Provisioning in Edge Assisted IoT Environment," in IEEE Access, vol. 11, pp. 27433-27449, 2023, doi: 10.1109/ACCESS.2023.3256277. A. Belenguer, J. Pascual and J. Naravidas, “GöwFed: A novel federated network intrusion detection system”, Journal of Network and Computer Applications, vol.217, pp. 103653-103668, 2023, doi: 10.1016/j.jnca.2023.103653. A. Ein Shoka, M. Dessouky, A. El-Sayed and E. El-Din Hemdan, “An efficient CNN based epileptic seizures detection framework using encrypted EEG signals for secure telemedicine applications”, Alexandria Engineering Journal, vol. 65, pp. 399-412, 2023, doi: 10.1016/j.aej.2022.10.014. A. Moradzadeh, H. Moayyed, B. Mohammadi-Ivatloo, Z. Vale, C. Ramos and R. Ghorbani, “A novel cyber-Resilient solar power forecasting model based on secure federated deep learning and data visualization”, Renewable Energy, vol.211, pp. 697-705, 2023, doi: 10.1016/j.renene.2023.04.055. A. Shukla, S. Srivastav, S. Kumar and P. Muhuri, “UInDeSI4.0: An efficient Unsupervised Intrusion Detection System for network traffic flow in Industry 4.0 ecosystem”, Engineering Applications of Artificial Intelligence, vol.120, pp. 105848-105858, 2023, doi: 10.1016/j.engappai.2023.105848. A. Smahi, H. Li, Y. Yang, X. Yang, P. Lu, Y. Zhong and C. Liu, “BV-ICVs: A privacy-preserving and verifiable federated learning framework for V2X environments using blockchain and zkSNARKs”, Journal of King Saud University - Computer and Information Sciences, vol.35, pp. 101542-101563, 2023, doi: 10.1016/j.jksuci.2023.03.020. A. Wahid, M. Msahli, A. Bifeta and G. Memmi, “NFA: A neural factorization autoencoder based online telephony fraud detection”, Digital Communications and Networks, pp. 1-15, 2023, doi: 10.1016/j.dcan.2023.03.002. A. Yang, C. Lu, J. Li, X. Huang, T. Ji, X. Li and Y. Sheng, “Application of meta-learning in cyberspace security: a survey”, Digital Communications and Networks, vol.9, pp. 67-78, 2023, doi: 10.1016/j.dcan.2022.03.007. A. Yang, Z. Ma, C. Zhang, Y. Han, Z. Hu, W. Zhang, X. Huang and Y. Wu, “Review on application progress of federated learning model and security hazard protection”, Digital Communications and Networks, vol.9, pp. 146-158, 2023, doi: 10.1016/j.dcan.2022.11.006. A. Yao, G. Li, X. Li, F. Jiang, J. Xu and X. Liu, “Differential privacy in edge computing-based smart city Applications:Security issues, solutions and future directions”, Array, vol.19, pp. 100293-100315, 2023, doi: 10.1016/j.array.2023.100293. B. Madhu, M. Venu Gopala Chari, R. Vankdothu, A. Silivery and V. Aerranagula, “Intrusion detection models for IOT networks via deep learning approaches”, Measurement: Sensors, vol. 25, pp. 100641-100655, 2023, doi: 10.1016/j.measen.2022.100641. D. Mishra, B. Naik, J. Nayak, A. Souri, P. Dash and S. Vimal, “Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network”, Digital Communications and Networks, vol.9, pp. 125-137, 2023, doi: 10.1016/j.dcan.2022.10.004. H. Altunay and Z. Albayrak, “A hybrid CNN + LSTM-based intrusion detection system for industrial IoT networks”, Engineering Science and Technology, an International Journal, vol. 38, pp. 101322-101335, 2023, doi: 10.1016/j.jestch.2022.101322. H. Kadry, A. Farouk, E. Zanaty and O. Reyad, “Intrusion detection model using optimized quantum neural network and elliptical curve cryptography for data security”, Alexandria Engineering Journal, vol. 71, pp. 491-500, 2023, doi: 10.1016/j.aej.2023.03.072. H. Liang, D. Liu, X. Zeng and C. Ye, "An Intrusion Detection Method for Advanced Metering Infrastructure System Based on Federated Learning," in Journal of Modern Power Systems and Clean Energy, vol. 11, no. 3, pp. 927-937, May 2023, doi: 10.35833/MPCE.2021.000279. H. -Y. Tran, J. Hu, X. Yin and H. R. Pota, "An Efficient Privacy-Enhancing Cross-Silo Federated Learning and Applications for False Data Injection Attack Detection in Smart Grids," in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2538-2552, 2023, doi: 10.1109/TIFS.2023.3267892. I. Kareem Thajeel, K. Samsudin, S. Jahari Hashim and F. Hashim, “Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model”, Journal of King Saud University - Computer and Information Sciences, vol.35, pp. 101490-101507, 2023, doi: 10.1016/j.jksuci.2023.01.012. J. Bhayo, S. Shah, S. Hameed, A. Ahmed, J. Nasir and D. Draheim, “Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks”, Engineering Applications of Artificial Intelligence, vol.123, pp. 106432-106449, 2023, doi: 10.1016/j.engappai.2023.106432. J. C. Bernal-Romero, J. M. Ramirez-Cortes, J. D. J. Rangel-Magdaleno, P. Gomez-Gil, H. Peregrina-Barreto and I. Cruz-Vega, "A Review on Protection and Cancelable Techniques in Biometric Systems," in IEEE Access, vol. 11, pp. 8531-8568, 2023, doi: 10.1109/ACCESS.2023.3239387. J. Lai, X. Song, R. Wang and X. Li, “Edge intelligent collaborative privacy protection solution for smart medical”, Cyber Security and Applications, vol.1, pp. 100010-100017, 2023, doi: 10.1016/j.csa.2022.100010. J. Yaacoub, H. Noura and O. Salman, “Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions”, Internet of Things and Cyber-Physical Systems, vol. 3, pp. 155-179, 2023, doi: 10.1016/j.iotcps.2023.04.001. J. Zhu, J. Wu, A. K. Bashir, Q. Pan and W. Yang, "Privacy-Preserving Federated Learning of Remote Sensing Image Classification With Dishonest Majority," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 4685-4698, 2023, doi: 10.1109/JSTARS.2023.3276781. K. Dhanya, S. Vajipayajula, K. Srinivasan, A. Tibrewal, T. Kumar and T. Kumar, “Detection of Network Attacks using Machine Learning and Deep Learning Models”, Procedia Computer Science, vol.218, pp. 57-66, 2023, doi: 10.1016/j.procs.2022.12.401. L. Ajao and S. Apeh, “Secure Edge Computing Vulnerabilities in Smart Cities Sustainability using Petri Net and Genetic Algorithm-based Reinforcement Learning”, Intelligent Systems with Applications, vol.18, pp. 200216-200237, 2023, doi: 10.1016/j.iswa.2023.200216. L. Khriji, S. Bouaafia, S. Messaoud, A. C. Ammari and M. Machhout, "Secure Convolutional Neural Network-Based Internet-of-Healthcare Applications," in IEEE Access, vol. 11, pp. 36787-36804, 2023, doi: 10.1109/ACCESS.2023.3266586. M. Butt, A. Qayyum, H. Ali, A. Al-Fuqaha and J. Qadir, “Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study”, Computers and Security, vol.125, pp. 103058-103075, 2023, doi: 10.1016/j.cose.2022.103058. M. Gomrokchi, S. Amin, H. Aboutalebi, A. Wong and D. Precup, "Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning," in IEEE Access, vol. 11, pp. 42796-42808, 2023, doi: 10.1109/ACCESS.2023.3270860. M. Habiba, Md. R. Islam, S. M. Muyeen and A. B. M. S. Ali, “Edge intelligence for network intrusion prevention in IoT ecosystem”, Computers and Electrical Engineering, vol 108 ,pp. 108727-108742, 2023, https://doi.org/10.1016/j.compeleceng.2023.108727. M. Khan, F. Glavin and M. Nickles, “Federated Learning as a Privacy Solution - An Overview”, Procedia Computer Science, vol. 217, pp. 316-325, 2023, doi: 10.1016/j.procs.2022.12.227 M. Mohammadi and I. Sohn, “AI based energy harvesting security methods: A survey”, ICT Express (2023), https://doi.org/10.1016/j.icte.2023.06.002. M. Venkatasubramanian, A. H. Lashkari and S. Hakak, "IoT Malware Analysis Using Federated Learning: A Comprehensive Survey," in IEEE Access, vol. 11, pp. 5004-5018, 2023, doi: 10.1109/ACCESS.2023.3235389. M. Vishwakarma and N. Kesswani, “A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelop method for anomaly detection”, Decision Analytics Journal, vol.7, pp.100233-100241, 2023, doi: 10.1016/j.dajour.2023.100233. Mirdula S. and Roopa M., “MUD enabled deep learning framework for anomaly detection in IoT integrated smart building”, e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol.5, pp. 100186-100201, 2023, doi: 10.1016/j.prime.2023.100186. Molins F, Serrano MA. Bases neurales de la aversión a las pérdidas en contextos económicos: revisión sistemática según las directrices PRISMA. Rev Neurol 2019; 68: 47-58. N. Jadav, R. kakkar, H. Mankodiya, R. Gupta, S. Tanwar, S. Agrawal and R. Sharma, “GRADE: Deep learning and garlic routing-based secure data sharing framework for IIoT beyond 5G”, Digital Communications and Networks, vol.9, pp.422-435, 2023, doi: 10.1016/j.dcan.2022.11.004. N. Omer, A. Samak, A. Taloba and R. Abd El-Aziz, “A novel optimized probabilistic neural network approach for intrusion detection and categorization”, Alexandria Engineering Journal, vol. 72, pp. 351-361, 2023, doi: 10.1016/j.aej.2023.03.093. N. Saran and N. Kesswani, “A comparative study of supervised Machine Learning classifiers for Intrusion Detection in Internet of Things”, Procedia Computer Science, vol. 218, pp. 2049-2057, 2023, doi: 10.1016/j.procs.2023.01.181. O. Bukhari, P. Agarwal, D. Koundal, S. Zafar, “Anomaly detection using ensemble techniques for boosting the security of intrusion detection system”, Procedia Computer Science, vol. 218, pp. 1003-1013, 2023, doi: 10.1016/j.procs.2023.01.080. O. Sanda, M. Pavlidis, S. seraj and N. Polatidis, “Long-Range attack detection on permissionless blockchains using Deep Learning”, Expert Systems with Applications, vol.218, pp.119606.-119616, 2023, doi: 10.1016/j.eswa.2023.119606. O. Y. Al-Jarrah, K. E. Haloui, M. Dianati and C. Maple, "A Novel Detection Approach of Unknown Cyber-Attacks for Intra-Vehicle Networks Using Recurrence Plots and Neural Networks," in IEEE Open Journal of Vehicular Technology, vol. 4, pp. 271-280, 2023, doi: 10.1109/OJVT.2023.3237802. P. Kumar, R. Kumar, G. Gupta, R. Tripathi, A. Jolfaeti and A. Najmul Islam, “A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system”, Journal of Parallel and Distributed Computing, vol.172, pp.69-83, 2023, doi: 10.1016/j.jpdc.2022.10.002. P. Ruzafa-Alcázar et al., "Intrusion Detection Based on Privacy-Preserving Federated Learning for the Industrial IoT," in IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1145-1154, Feb. 2023, doi: 10.1109/TII.2021.3126728. Q. Abu Al-Haija, “Cost-effective detection system of cross-site scripting attacks using hybrid learning approach”, Results in Engineering, vol.19, pp. 101266-101274, 2023, doi: 10.1016/j.rineng.2023.101266. R. Banavathu and S. Meruva, “Efficient secure data storage based on novel blockchain model over IoT-based smart computing systems”, Measurement: Sensors, vol. 27, pp. 100741-100749, 2023, doi: 10.1016/j.measen.2023.100741. R. Elsayed, R. Hamada, M. Abdalla and S. Elsaid, “Securing IoT and SDN systems using deep-learning based automatic intrusion detection”, Ain Shams Engineering Journal, vol.14, pp. 102211-102224, 2023, doi: 10.1016/j.asej.2023.102211. R. Ettiyan and V. Geetha, “A hybrid logistic DNA-based encryption system for securing the Internet of Things patient monitoring systems”, Healthcare Analytics, vol.3, pp. 100149-100157, 2023, doi: 10.1016/j.health.2023.100149. R. Golchha, A. Joshi and G. Gupta, “Voting-based Ensemble Learning approach for Cyber Attacks Detection in Industrial Internet of Things”, Procedia Computer Science, vol.218, pp. 1752-1759, 2023, doi: 10.1016/j.procs.2023.01.153. R. Parekh et al., "GeFL: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles," in IEEE Access, vol. 11, pp. 1825-1839, 2023, doi: 10.1109/ACCESS.2023.3233983. R. Satyanarayana and K. Selvakumar, “Bi-linear mapping integrated machine learning based authentication routing protocol for improving quality of service in vehicular Ad-Hoc network”, e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 4, pp. 100145-100161, 2023, doi: 10.1016/j.prime.2023.100145 S. Frimpong, M. Han, E. Boahen, R. Ayitey Sosu, I. Hanson, O. Larbi-Siaw and I. Senkyire, “RecGuard: An efficient privacy preservation blockchain-based system for online social network users”, Blockchain: Research and Applications, vol. 4, pp. 100111-100123, 2023, doi: 10.1016/j.bcra.2022.100111. S. Kanchan, J. Jang, J. Yoon and B. Choi, “Efficient and privacy-preserving group signature for federated learning”, Future Generation Computer Systems, vol. 147, pp. 93-106, 2023, doi: 10.1016/j.future.2023.04.017. S. Rani, A. Kataria, S.kumar and P. Tiwari, “Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review”, Knowledge-Based Systems, vol. 274, pp. 110658-110686, 2023, doi: 10.1016/j.knosys.2023.110658. S. Wagan, J. Koo, I. Siddiqui, N. Qureshi, M. Attique and D. Shin, “A Fuzzy-Based Duo-Secure Multi-Modal Framework for IoMT Anomaly Detection”, Journal of King Saud University - Computer and Information Sciences, vol.35, pp. 131-144, 2023, doi: 10.1016/j.jksuci.2022.11.007. S. Yaqoob, A. Hussain, F. Subhan, G. Pappalardo and M. Awais, "Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network," in IEEE Access, vol. 11, pp. 19024-19038, 2023, doi: 10.1109/ACCESS.2023.3246660. Tirumala G., S. Fiza, A. Kumar, V. Devi, C. Kumar and A. Kubra, “Improved chimp optimization algorithm (ICOA) feature selection and deep neural network framework for internet of things (IOT) based android malware detection”, Measurement: Sensors, pp. 100785-100810, 2023, doi: 10.1016/j.measen.2023.100785. V. Gugueoth, S. Safavat and S. Shetty, Security of Internet of Things (IoT) using federated learning and deep learning — Recent advancements, issues and prospects, ICT Express (2023), https://doi.org/10.1016/j.icte.2023.03.006. V. Terziyan, D. Malyk, M. Golovianko and V. Branytskyi, “Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing”, Procedia Computer Science, vol. 217, pp. 91-101, 2023, doi: 10.1016/j.procs.2022.12.205. X. Sáez-de-Cámara, J. Flores, C. Arellano, A. Urbieta, U. Zurutuza, “Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networks”, Computers & Security, vol.131, pp.103299-103319, 2023, doi: 10.1016/j.cose.2023.103299. Y. S. Hindistan and E. F. Yetkin, "A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data," in IEEE Access, vol. 11, pp. 5837-5849, 2023, doi: 10.1109/ACCESS.2023.3235969. Yepes-Nuñez JJ, et al. Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas. Rev Esp Cardiol. 2021. https://doi.org/10.1016/j.recesp.2021.06.016 |
| dc.rights.uri.none.fl_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/closedAccess |
| dc.rights.creativecommons.none.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
| dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_14cb |
| rights_invalid_str_mv |
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_14cb |
| eu_rights_str_mv |
closedAccess |
| dc.format.extent.none.fl_str_mv |
38 p. |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería de Sistemas, Cali |
| dc.publisher.program.none.fl_str_mv |
Ingeniería de Sistemas |
| dc.publisher.faculty.none.fl_str_mv |
Ingenierías |
| dc.publisher.place.none.fl_str_mv |
Cali |
| dc.publisher.branch.none.fl_str_mv |
Cali |
| publisher.none.fl_str_mv |
Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería de Sistemas, Cali |
| institution |
Universidad Cooperativa de Colombia |
| bitstream.url.fl_str_mv |
https://repository.ucc.edu.co/bitstreams/f47be551-7d79-4033-82c5-c480d67852dc/download https://repository.ucc.edu.co/bitstreams/7230b800-7d3c-4079-98ee-c42dfac41a0e/download https://repository.ucc.edu.co/bitstreams/cc42c2c2-2991-43eb-bbe6-0e9417401a3c/download https://repository.ucc.edu.co/bitstreams/883eef23-89f9-44bf-883c-9f4f84a3b2d1/download https://repository.ucc.edu.co/bitstreams/10aac70f-e312-463d-93e5-71f347090ddd/download https://repository.ucc.edu.co/bitstreams/5fd0cbc7-9995-45af-aa35-254dee3dc4e7/download https://repository.ucc.edu.co/bitstreams/4cb8b14f-d334-4776-b022-2c4c0ac19947/download https://repository.ucc.edu.co/bitstreams/b809d49f-5945-4cd9-9ab4-745214144bec/download https://repository.ucc.edu.co/bitstreams/6074f8c7-1fca-4738-bace-e3ce4e98c804/download https://repository.ucc.edu.co/bitstreams/389ff686-a752-4cfc-96ba-69fff4667a18/download |
| bitstream.checksum.fl_str_mv |
905ec83c529cc731887908963ef968b2 8803976a7b225b1497e4a2e36f3934a8 76c6a784aca4048c9000f9e5087ed42f 3bce4f7ab09dfc588f126e1e36e98a45 3d9241896411df7ff8b6b05d96b3e722 e1c06d85ae7b8b032bef47e42e4c08f9 6d93d3216dc4a7f5df47d4876fbec4d3 e897e1241d2f66fc03e571ddc32e1da3 7cde470851125bb2e7ddc8773819a980 eab8bf82f30bca5193a84097c3939bd5 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Cooperativa de Colombia |
| repository.mail.fl_str_mv |
bdigital@metabiblioteca.com |
| _version_ |
1814246766038482944 |
| spelling |
Cano Beltrán, Jhon HaideBedoya Ocampo, Luis Miguel2024-06-18T21:40:29Z2024-06-18T21:40:29Z2024Bedoya Campo, L. M. (2024). Inteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prisma [Tesis de pregrado, Universidad Cooperativa de Colombia] Repositorio Institucional Universidad Cooperativa de Colombia. https://hdl.handle.net/20.500.12494/55992https://hdl.handle.net/20.500.12494/55992Los continuos avances en la tecnología, destinados a satisfacer las demandas cambiantes de la sociedad actual, han sumergido a la humanidad en un entorno cada vez más digitalizado. Este fenómeno se traduce en un constante aumento de dispositivos conectados a través de internet, generando una masiva transferencia de información y dando origen al Internet de las Cosas (IdC). Ante este panorama, es crucial que el IdC preste especial atención a posibles accesos no autorizados o manipulaciones de información sensible presentes en el flujo de datos de sus redes. En este contexto, la Inteligencia Artificial (IA) emerge como un valioso aliado tecnológico en cuestiones de seguridad y privacidad en las redes. Su papel fundamental radica en garantizar la confidencialidad de la información. Este trabajo se centrará en explorar la contribución de la inteligencia artificial para asegurar la seguridad y privacidad en entornos de red.Continuous advances in technology, aimed at meeting the changing demands of today's society, have immersed humanity in an increasingly digitalized environment. This phenomenon translates into a constant increase in devices connected through the Internet, generating a massive transfer of information and giving rise to the Internet of Things (IoT). Given this scenario, it is crucial that the IoT pays special attention to possible unauthorized access or manipulation of sensitive information present in the data flow of its networks. In this context, Artificial Intelligence (AI) emerges as a valuable technological ally in issues of security and privacy on networks. Its fundamental role lies in guaranteeing the confidentiality of the information. This work will focus on exploring the contribution of artificial intelligence to ensuring security and privacy in network environments.1. Resumen -- Introducción -- 2. Planteamiento del Problema -- 3. Objetivo general -- 3.1. Objetivos específicos -- 4. Justificación -- 5. Metodología -- 6. Resultados -- 7. Conclusiones -- 8. Recomendaciones -- 9. Referencias --PregradoIngeniero de Sistemas38 p.application/pdfspaUniversidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería de Sistemas, CaliIngeniería de SistemasIngenieríasCaliCalihttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_14cb000 - Ciencias de la computación, información y obras generalesI.ASeguridadDirectricesPrismaA.ISecurityGuidelinesPrismInteligencia artificial aplicada a la seguridad y privacidad en las redes: revisión sistemática de la literatura según las directrices prismaTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttp://purl.org/redcol/resource_type/TPinfo:eu-repo/semantics/acceptedVersionA. A. M. Sharadqh, H. A. M. Hatamleh, A. M. A. Alnaser, S. S. Saloum and T. A. Alawneh, "Hybrid Chain: Blockchain Enabled Framework for Bi-Level Intrusion Detection and Graph-Based Mitigation for Security Provisioning in Edge Assisted IoT Environment," in IEEE Access, vol. 11, pp. 27433-27449, 2023, doi: 10.1109/ACCESS.2023.3256277.A. Belenguer, J. Pascual and J. Naravidas, “GöwFed: A novel federated network intrusion detection system”, Journal of Network and Computer Applications, vol.217, pp. 103653-103668, 2023, doi: 10.1016/j.jnca.2023.103653.A. Ein Shoka, M. Dessouky, A. El-Sayed and E. El-Din Hemdan, “An efficient CNN based epileptic seizures detection framework using encrypted EEG signals for secure telemedicine applications”, Alexandria Engineering Journal, vol. 65, pp. 399-412, 2023, doi: 10.1016/j.aej.2022.10.014.A. Moradzadeh, H. Moayyed, B. Mohammadi-Ivatloo, Z. Vale, C. Ramos and R. Ghorbani, “A novel cyber-Resilient solar power forecasting model based on secure federated deep learning and data visualization”, Renewable Energy, vol.211, pp. 697-705, 2023, doi: 10.1016/j.renene.2023.04.055.A. Shukla, S. Srivastav, S. Kumar and P. Muhuri, “UInDeSI4.0: An efficient Unsupervised Intrusion Detection System for network traffic flow in Industry 4.0 ecosystem”, Engineering Applications of Artificial Intelligence, vol.120, pp. 105848-105858, 2023, doi: 10.1016/j.engappai.2023.105848.A. Smahi, H. Li, Y. Yang, X. Yang, P. Lu, Y. Zhong and C. Liu, “BV-ICVs: A privacy-preserving and verifiable federated learning framework for V2X environments using blockchain and zkSNARKs”, Journal of King Saud University - Computer and Information Sciences, vol.35, pp. 101542-101563, 2023, doi: 10.1016/j.jksuci.2023.03.020.A. Wahid, M. Msahli, A. Bifeta and G. Memmi, “NFA: A neural factorization autoencoder based online telephony fraud detection”, Digital Communications and Networks, pp. 1-15, 2023, doi: 10.1016/j.dcan.2023.03.002.A. Yang, C. Lu, J. Li, X. Huang, T. Ji, X. Li and Y. Sheng, “Application of meta-learning in cyberspace security: a survey”, Digital Communications and Networks, vol.9, pp. 67-78, 2023, doi: 10.1016/j.dcan.2022.03.007.A. Yang, Z. Ma, C. Zhang, Y. Han, Z. Hu, W. Zhang, X. Huang and Y. Wu, “Review on application progress of federated learning model and security hazard protection”, Digital Communications and Networks, vol.9, pp. 146-158, 2023, doi: 10.1016/j.dcan.2022.11.006.A. Yao, G. Li, X. Li, F. Jiang, J. Xu and X. Liu, “Differential privacy in edge computing-based smart city Applications:Security issues, solutions and future directions”, Array, vol.19, pp. 100293-100315, 2023, doi: 10.1016/j.array.2023.100293.B. Madhu, M. Venu Gopala Chari, R. Vankdothu, A. Silivery and V. Aerranagula, “Intrusion detection models for IOT networks via deep learning approaches”, Measurement: Sensors, vol. 25, pp. 100641-100655, 2023, doi: 10.1016/j.measen.2022.100641.D. Mishra, B. Naik, J. Nayak, A. Souri, P. Dash and S. Vimal, “Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network”, Digital Communications and Networks, vol.9, pp. 125-137, 2023, doi: 10.1016/j.dcan.2022.10.004.H. Altunay and Z. Albayrak, “A hybrid CNN + LSTM-based intrusion detection system for industrial IoT networks”, Engineering Science and Technology, an International Journal, vol. 38, pp. 101322-101335, 2023, doi: 10.1016/j.jestch.2022.101322.H. Kadry, A. Farouk, E. Zanaty and O. Reyad, “Intrusion detection model using optimized quantum neural network and elliptical curve cryptography for data security”, Alexandria Engineering Journal, vol. 71, pp. 491-500, 2023, doi: 10.1016/j.aej.2023.03.072.H. Liang, D. Liu, X. Zeng and C. Ye, "An Intrusion Detection Method for Advanced Metering Infrastructure System Based on Federated Learning," in Journal of Modern Power Systems and Clean Energy, vol. 11, no. 3, pp. 927-937, May 2023, doi: 10.35833/MPCE.2021.000279.H. -Y. Tran, J. Hu, X. Yin and H. R. Pota, "An Efficient Privacy-Enhancing Cross-Silo Federated Learning and Applications for False Data Injection Attack Detection in Smart Grids," in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2538-2552, 2023, doi: 10.1109/TIFS.2023.3267892.I. Kareem Thajeel, K. Samsudin, S. Jahari Hashim and F. Hashim, “Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model”, Journal of King Saud University - Computer and Information Sciences, vol.35, pp. 101490-101507, 2023, doi: 10.1016/j.jksuci.2023.01.012.J. Bhayo, S. Shah, S. Hameed, A. Ahmed, J. Nasir and D. Draheim, “Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks”, Engineering Applications of Artificial Intelligence, vol.123, pp. 106432-106449, 2023, doi: 10.1016/j.engappai.2023.106432.J. C. Bernal-Romero, J. M. Ramirez-Cortes, J. D. J. Rangel-Magdaleno, P. Gomez-Gil, H. Peregrina-Barreto and I. Cruz-Vega, "A Review on Protection and Cancelable Techniques in Biometric Systems," in IEEE Access, vol. 11, pp. 8531-8568, 2023, doi: 10.1109/ACCESS.2023.3239387.J. Lai, X. Song, R. Wang and X. Li, “Edge intelligent collaborative privacy protection solution for smart medical”, Cyber Security and Applications, vol.1, pp. 100010-100017, 2023, doi: 10.1016/j.csa.2022.100010.J. Yaacoub, H. Noura and O. Salman, “Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions”, Internet of Things and Cyber-Physical Systems, vol. 3, pp. 155-179, 2023, doi: 10.1016/j.iotcps.2023.04.001.J. Zhu, J. Wu, A. K. Bashir, Q. Pan and W. Yang, "Privacy-Preserving Federated Learning of Remote Sensing Image Classification With Dishonest Majority," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 4685-4698, 2023, doi: 10.1109/JSTARS.2023.3276781.K. Dhanya, S. Vajipayajula, K. Srinivasan, A. Tibrewal, T. Kumar and T. Kumar, “Detection of Network Attacks using Machine Learning and Deep Learning Models”, Procedia Computer Science, vol.218, pp. 57-66, 2023, doi: 10.1016/j.procs.2022.12.401.L. Ajao and S. Apeh, “Secure Edge Computing Vulnerabilities in Smart Cities Sustainability using Petri Net and Genetic Algorithm-based Reinforcement Learning”, Intelligent Systems with Applications, vol.18, pp. 200216-200237, 2023, doi: 10.1016/j.iswa.2023.200216.L. Khriji, S. Bouaafia, S. Messaoud, A. C. Ammari and M. Machhout, "Secure Convolutional Neural Network-Based Internet-of-Healthcare Applications," in IEEE Access, vol. 11, pp. 36787-36804, 2023, doi: 10.1109/ACCESS.2023.3266586.M. Butt, A. Qayyum, H. Ali, A. Al-Fuqaha and J. Qadir, “Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study”, Computers and Security, vol.125, pp. 103058-103075, 2023, doi: 10.1016/j.cose.2022.103058.M. Gomrokchi, S. Amin, H. Aboutalebi, A. Wong and D. Precup, "Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning," in IEEE Access, vol. 11, pp. 42796-42808, 2023, doi: 10.1109/ACCESS.2023.3270860.M. Habiba, Md. R. Islam, S. M. Muyeen and A. B. M. S. Ali, “Edge intelligence for network intrusion prevention in IoT ecosystem”, Computers and Electrical Engineering, vol 108 ,pp. 108727-108742, 2023, https://doi.org/10.1016/j.compeleceng.2023.108727.M. Khan, F. Glavin and M. Nickles, “Federated Learning as a Privacy Solution - An Overview”, Procedia Computer Science, vol. 217, pp. 316-325, 2023, doi: 10.1016/j.procs.2022.12.227M. Mohammadi and I. Sohn, “AI based energy harvesting security methods: A survey”, ICT Express (2023), https://doi.org/10.1016/j.icte.2023.06.002.M. Venkatasubramanian, A. H. Lashkari and S. Hakak, "IoT Malware Analysis Using Federated Learning: A Comprehensive Survey," in IEEE Access, vol. 11, pp. 5004-5018, 2023, doi: 10.1109/ACCESS.2023.3235389.M. Vishwakarma and N. Kesswani, “A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelop method for anomaly detection”, Decision Analytics Journal, vol.7, pp.100233-100241, 2023, doi: 10.1016/j.dajour.2023.100233.Mirdula S. and Roopa M., “MUD enabled deep learning framework for anomaly detection in IoT integrated smart building”, e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol.5, pp. 100186-100201, 2023, doi: 10.1016/j.prime.2023.100186.Molins F, Serrano MA. Bases neurales de la aversión a las pérdidas en contextos económicos: revisión sistemática según las directrices PRISMA. Rev Neurol 2019; 68: 47-58.N. Jadav, R. kakkar, H. Mankodiya, R. Gupta, S. Tanwar, S. Agrawal and R. Sharma, “GRADE: Deep learning and garlic routing-based secure data sharing framework for IIoT beyond 5G”, Digital Communications and Networks, vol.9, pp.422-435, 2023, doi: 10.1016/j.dcan.2022.11.004.N. Omer, A. Samak, A. Taloba and R. Abd El-Aziz, “A novel optimized probabilistic neural network approach for intrusion detection and categorization”, Alexandria Engineering Journal, vol. 72, pp. 351-361, 2023, doi: 10.1016/j.aej.2023.03.093.N. Saran and N. Kesswani, “A comparative study of supervised Machine Learning classifiers for Intrusion Detection in Internet of Things”, Procedia Computer Science, vol. 218, pp. 2049-2057, 2023, doi: 10.1016/j.procs.2023.01.181.O. Bukhari, P. Agarwal, D. Koundal, S. Zafar, “Anomaly detection using ensemble techniques for boosting the security of intrusion detection system”, Procedia Computer Science, vol. 218, pp. 1003-1013, 2023, doi: 10.1016/j.procs.2023.01.080.O. Sanda, M. Pavlidis, S. seraj and N. Polatidis, “Long-Range attack detection on permissionless blockchains using Deep Learning”, Expert Systems with Applications, vol.218, pp.119606.-119616, 2023, doi: 10.1016/j.eswa.2023.119606.O. Y. Al-Jarrah, K. E. Haloui, M. Dianati and C. Maple, "A Novel Detection Approach of Unknown Cyber-Attacks for Intra-Vehicle Networks Using Recurrence Plots and Neural Networks," in IEEE Open Journal of Vehicular Technology, vol. 4, pp. 271-280, 2023, doi: 10.1109/OJVT.2023.3237802.P. Kumar, R. Kumar, G. Gupta, R. Tripathi, A. Jolfaeti and A. Najmul Islam, “A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system”, Journal of Parallel and Distributed Computing, vol.172, pp.69-83, 2023, doi: 10.1016/j.jpdc.2022.10.002.P. Ruzafa-Alcázar et al., "Intrusion Detection Based on Privacy-Preserving Federated Learning for the Industrial IoT," in IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1145-1154, Feb. 2023, doi: 10.1109/TII.2021.3126728.Q. Abu Al-Haija, “Cost-effective detection system of cross-site scripting attacks using hybrid learning approach”, Results in Engineering, vol.19, pp. 101266-101274, 2023, doi: 10.1016/j.rineng.2023.101266.R. Banavathu and S. Meruva, “Efficient secure data storage based on novel blockchain model over IoT-based smart computing systems”, Measurement: Sensors, vol. 27, pp. 100741-100749, 2023, doi: 10.1016/j.measen.2023.100741.R. Elsayed, R. Hamada, M. Abdalla and S. Elsaid, “Securing IoT and SDN systems using deep-learning based automatic intrusion detection”, Ain Shams Engineering Journal, vol.14, pp. 102211-102224, 2023, doi: 10.1016/j.asej.2023.102211.R. Ettiyan and V. Geetha, “A hybrid logistic DNA-based encryption system for securing the Internet of Things patient monitoring systems”, Healthcare Analytics, vol.3, pp. 100149-100157, 2023, doi: 10.1016/j.health.2023.100149.R. Golchha, A. Joshi and G. Gupta, “Voting-based Ensemble Learning approach for Cyber Attacks Detection in Industrial Internet of Things”, Procedia Computer Science, vol.218, pp. 1752-1759, 2023, doi: 10.1016/j.procs.2023.01.153.R. Parekh et al., "GeFL: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles," in IEEE Access, vol. 11, pp. 1825-1839, 2023, doi: 10.1109/ACCESS.2023.3233983.R. Satyanarayana and K. Selvakumar, “Bi-linear mapping integrated machine learning based authentication routing protocol for improving quality of service in vehicular Ad-Hoc network”, e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 4, pp. 100145-100161, 2023, doi: 10.1016/j.prime.2023.100145S. Frimpong, M. Han, E. Boahen, R. Ayitey Sosu, I. Hanson, O. Larbi-Siaw and I. Senkyire, “RecGuard: An efficient privacy preservation blockchain-based system for online social network users”, Blockchain: Research and Applications, vol. 4, pp. 100111-100123, 2023, doi: 10.1016/j.bcra.2022.100111.S. Kanchan, J. Jang, J. Yoon and B. Choi, “Efficient and privacy-preserving group signature for federated learning”, Future Generation Computer Systems, vol. 147, pp. 93-106, 2023, doi: 10.1016/j.future.2023.04.017.S. Rani, A. Kataria, S.kumar and P. Tiwari, “Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review”, Knowledge-Based Systems, vol. 274, pp. 110658-110686, 2023, doi: 10.1016/j.knosys.2023.110658.S. Wagan, J. Koo, I. Siddiqui, N. Qureshi, M. Attique and D. Shin, “A Fuzzy-Based Duo-Secure Multi-Modal Framework for IoMT Anomaly Detection”, Journal of King Saud University - Computer and Information Sciences, vol.35, pp. 131-144, 2023, doi: 10.1016/j.jksuci.2022.11.007.S. Yaqoob, A. Hussain, F. Subhan, G. Pappalardo and M. Awais, "Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network," in IEEE Access, vol. 11, pp. 19024-19038, 2023, doi: 10.1109/ACCESS.2023.3246660.Tirumala G., S. Fiza, A. Kumar, V. Devi, C. Kumar and A. Kubra, “Improved chimp optimization algorithm (ICOA) feature selection and deep neural network framework for internet of things (IOT) based android malware detection”, Measurement: Sensors, pp. 100785-100810, 2023, doi: 10.1016/j.measen.2023.100785.V. Gugueoth, S. Safavat and S. Shetty, Security of Internet of Things (IoT) using federated learning and deep learning — Recent advancements, issues and prospects, ICT Express (2023), https://doi.org/10.1016/j.icte.2023.03.006.V. Terziyan, D. Malyk, M. Golovianko and V. Branytskyi, “Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing”, Procedia Computer Science, vol. 217, pp. 91-101, 2023, doi: 10.1016/j.procs.2022.12.205.X. Sáez-de-Cámara, J. Flores, C. Arellano, A. Urbieta, U. Zurutuza, “Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networks”, Computers & Security, vol.131, pp.103299-103319, 2023, doi: 10.1016/j.cose.2023.103299.Y. S. Hindistan and E. F. Yetkin, "A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data," in IEEE Access, vol. 11, pp. 5837-5849, 2023, doi: 10.1109/ACCESS.2023.3235969.Yepes-Nuñez JJ, et al. Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas. Rev Esp Cardiol. 2021. https://doi.org/10.1016/j.recesp.2021.06.016PublicationORIGINAL2024_Trabajo_Grado.pdf2024_Trabajo_Grado.pdfapplication/pdf509234https://repository.ucc.edu.co/bitstreams/f47be551-7d79-4033-82c5-c480d67852dc/download905ec83c529cc731887908963ef968b2MD532024_Acta_Sustentacion.pdf2024_Acta_Sustentacion.pdfapplication/pdf31891https://repository.ucc.edu.co/bitstreams/7230b800-7d3c-4079-98ee-c42dfac41a0e/download8803976a7b225b1497e4a2e36f3934a8MD512024_Licencia_Uso.pdf2024_Licencia_Uso.pdfapplication/pdf1375335https://repository.ucc.edu.co/bitstreams/cc42c2c2-2991-43eb-bbe6-0e9417401a3c/download76c6a784aca4048c9000f9e5087ed42fMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-84334https://repository.ucc.edu.co/bitstreams/883eef23-89f9-44bf-883c-9f4f84a3b2d1/download3bce4f7ab09dfc588f126e1e36e98a45MD54TEXT2024_Trabajo_Grado.pdf.txt2024_Trabajo_Grado.pdf.txtExtracted texttext/plain51706https://repository.ucc.edu.co/bitstreams/10aac70f-e312-463d-93e5-71f347090ddd/download3d9241896411df7ff8b6b05d96b3e722MD592024_Acta_Sustentacion.pdf.txt2024_Acta_Sustentacion.pdf.txtExtracted texttext/plain2https://repository.ucc.edu.co/bitstreams/5fd0cbc7-9995-45af-aa35-254dee3dc4e7/downloade1c06d85ae7b8b032bef47e42e4c08f9MD552024_Licencia_Uso.pdf.txt2024_Licencia_Uso.pdf.txtExtracted texttext/plain6https://repository.ucc.edu.co/bitstreams/4cb8b14f-d334-4776-b022-2c4c0ac19947/download6d93d3216dc4a7f5df47d4876fbec4d3MD57THUMBNAIL2024_Trabajo_Grado.pdf.jpg2024_Trabajo_Grado.pdf.jpgGenerated Thumbnailimage/jpeg6272https://repository.ucc.edu.co/bitstreams/b809d49f-5945-4cd9-9ab4-745214144bec/downloade897e1241d2f66fc03e571ddc32e1da3MD5102024_Acta_Sustentacion.pdf.jpg2024_Acta_Sustentacion.pdf.jpgGenerated Thumbnailimage/jpeg10967https://repository.ucc.edu.co/bitstreams/6074f8c7-1fca-4738-bace-e3ce4e98c804/download7cde470851125bb2e7ddc8773819a980MD562024_Licencia_Uso.pdf.jpg2024_Licencia_Uso.pdf.jpgGenerated Thumbnailimage/jpeg10702https://repository.ucc.edu.co/bitstreams/389ff686-a752-4cfc-96ba-69fff4667a18/downloadeab8bf82f30bca5193a84097c3939bd5MD5820.500.12494/55992oai:repository.ucc.edu.co:20.500.12494/559922024-08-21 17:22:19.476https://creativecommons.org/licenses/by-nc-nd/4.0/Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)restrictedhttps://repository.ucc.edu.coRepositorio Institucional Universidad Cooperativa de Colombiabdigital@metabiblioteca.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 |