Un método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las Cosas

ilustraciones, diagramas, tablas

Autores:: Parra Jiménez, Jhon Alexander

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Un método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las Cosas
dc.title.translated.eng.fl_str_mv	A Method Based on Deep Learning for the Early Detection and Characterization of Cybersecurity Incidents in Internet of Things Devices
title	Un método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las Cosas
spellingShingle	Un método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las Cosas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Computer Storage Devices Dispositivos de almacenamiento (Computadores) Firewalls (Computer science) Firewalls (Computadores) Internet de las Cosas Ciberseguridad Aprendizaje Profundo Amenazas Redes Convolucionales Redes Recurrentes IoT Cibersecurity Deep Learning DoS Convolutional Neural Network Recurrent Neural Network
title_short	Un método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las Cosas
title_full	Un método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las Cosas
title_fullStr	Un método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las Cosas
title_full_unstemmed	Un método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las Cosas
title_sort	Un método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las Cosas
dc.creator.fl_str_mv	Parra Jiménez, Jhon Alexander
dc.contributor.advisor.none.fl_str_mv	Gutiérrez Betancur, Sergio Armando Branch Bedoya, John Willian
dc.contributor.author.none.fl_str_mv	Parra Jiménez, Jhon Alexander
dc.contributor.researchgroup.spa.fl_str_mv	Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Computer Storage Devices Dispositivos de almacenamiento (Computadores) Firewalls (Computer science) Firewalls (Computadores) Internet de las Cosas Ciberseguridad Aprendizaje Profundo Amenazas Redes Convolucionales Redes Recurrentes IoT Cibersecurity Deep Learning DoS Convolutional Neural Network Recurrent Neural Network
dc.subject.lemb.none.fl_str_mv	Computer Storage Devices Dispositivos de almacenamiento (Computadores) Firewalls (Computer science) Firewalls (Computadores)
dc.subject.proposal.spa.fl_str_mv	Internet de las Cosas Ciberseguridad Aprendizaje Profundo Amenazas Redes Convolucionales Redes Recurrentes
dc.subject.proposal.eng.fl_str_mv	IoT Cibersecurity Deep Learning DoS Convolutional Neural Network Recurrent Neural Network
description	ilustraciones, diagramas, tablas
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-12-01
dc.date.accessioned.none.fl_str_mv	2022-03-08T14:48:28Z
dc.date.available.none.fl_str_mv	2022-03-08T14:48:28Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
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dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81148
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81148 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Abbas, S., Faisal, M., Rahman, H. U., Khan, M. Z., Merabti, M., & Khan, A. U. (2018). Masquerading attacks detection in mobile ad hoc networks. IEEE Access, 55013- 55025. Alaba, F. A., Othman, M., Hashem, I. A., & Alotaibi, F. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications, 10-28. Al-Garadi, M. A., Mohamed, A., Al-Ali, A., Du, X., & Guizani, M. (2020). A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security. IEEE Communications Surveys & Tutorials. Alguliyev, R., Imamverdiyev, Y., & Sukhostat, L. (2018). Cyber-physical systems and their security issues. Computers in Industry, 100, 212-223. Amanullah, M. A., Habeeb, R. A., Nasaruddin, F. H., Gani, A., Ahmed, E., Nainar, A. S., . . . Imran, M. (2020). Deep learning and big data technologies for IoT security. Computer Communications. Anand, P., Singh, Y., Selwal, A., Alazab, M., Tanwar, S., & Kumar, N. (2020). IoT Vulnerability Assessment for Sustainable Computing: Threats, Current Solutions, and Open Challenges. IEEE Access, 168825-168853. Baig, Z. A., Sanguanpong, S., Firdous, S. N., Vo, V. N., Nguyen, T. G., & So-In, C. (2020). Averaged dependence estimators for DoS attack detection in IoT networks. Future Generation Computer Systems, 102, 198-209. Biswas, S., Sajal, M., Afrin, T., Bhuiyan, T., & Hassan, M. (2018). A study on remote code execution vulnerability in web applications. International Conference on Cyber Security and Computer Science (ICONCS 2018). Safranbolu. Bošnjak, L., Sreš, J., & Brumen, B. (2018). Brute-force and dictionary attack on hashed real-world passwords. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (págs. 1161-1166). IEEE. Brun, O., Yin, Y., Gelenbe, E., Kadioglu, Y. M., Augusto-Gonzalez, J., & Ramos, M. (2018). Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments. International ISCIS Security Workshop (págs. 79-89). Springer. Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2018). Network Intrusion Detection for IoT Security based on Learning Techniques. IEEE Communications Surveys & Tutorials, 2671-2701. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (págs. 1251-1258). IEEE. Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N., & Qin, J. (2018). A survey on application of machine learning for Internet of Things. International Journal of Machine Learning and Cybernetics. Deogirikar, J., & Vidhate, A. (2017). Security attacks in IoT: A survey. 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), (págs. 32-37). Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 761-768. Doshi, R., Apthorpe, N., & Feamster, N. (2018). Machine Learning DDoS Detection for Consumer Internet of Things Devices. 2018 IEEE Security and Privacy Workshops (SPW), 29-35. Elrawy, M. F., Awad, A. I., & Hamed, H. F. (2018). Intrusion detection systems for IoT- based smart environments: a survey. Journal of Cloud Computing, 1-20. Frustaci, M., Pace, P., Aloi, G., & Fortino, G. (2017). Evaluating critical security issues of the IoT world: Present and future challenges. IEEE Internet of things journal, 5(4), 2483-2495. Galeano-Brajones, J., Carmona-Murillo, J., Valenzuela-Valdés, J. F., & Luna-Valero, F. (2020). Detection and Mitigation of DoS and DDoS Attacks in IoT-Based Stateful SDN: An Experimental Approach. Sensors. Garg, S., Aujla, G. S., Kumar, N., & Batra, S. (2019). Tree-based attack--defense model for risk assessment in multi-UAV networks. 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Cyber-Physical Systems Security – A Survey. IEEE Internet of Things Journal, 4(6), 1802-1831. Hussain, F., Abbas, S. G., Husnain, M., Fayyaz, U. U., Shahzad, F., & Shah, G. A. (2020). IoT DoS and DDoS Attack Detection using ResNet. arXiv preprint arXiv:2012.01971. Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020). Machine Learning in IoT Security: Current Solutions and Future Challenges. IEEE Communications Surveys & Tutorials. Ibitoye, O., Shafiq, O., & Matrawy, A. (2019). Analyzing adversarial attacks against deep learning for intrusion detection in IoT networks. 2019 IEEE Global Communications Conference (GLOBECOM) (págs. 1-6). IEEE. Icontec. (2017). NTC-ISO-IEC Tecnología de la información. Técnicas de seguridad. Sistemas de gestión de seguridad de la información (SGSI). Visión general y vocabulario. Injadat, M., Moubayed, A., & Shami, A. (2020). Detecting botnet attacks in IoT environments: an optimized machine learning approach. arXiv preprint arXiv:2012.11325. 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Recuperado el 2 de Mayo de 2021, de https://keras.io/api/applications/ Lu, Y., & Da Xu, L. (2018). Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics. IEEE Internet of Things Journal, 6(2), 2103-2115. Makhdoom, I., Abolhasan, M., Lipman, J., Liu, R. P., & Ni, W. (2018). Anatomy of Threats to the Internet of Things. IEEE Communications Surveys & Tutorials, 1636-1675. Meneghello, F., Calore, M., Zucchetto, D., Polese, M., & Zanella, A. (2019). IoT: Internet of Threats? A survey of practical security vulnerabilities in real IoT devices. IEEE Internet of Things Journal, 6(5), 8182-8201. Miloslavskaya, N., & Tolstoy, A. (2018). Internet of Things: information security challenges and solutions. Cluster Computing, 103-119. Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020). Survey on IoT Security:Challenges and Solution using Machine Learning, Artificial Intelligence and Blockchain Technology. Internet of Things, 100227. Mossberger, K., Tolbert, C., & McNeal, R. S. (2007). Digital citizenship: The Internet, society, and participation. MIT Press. Neshenko, N., Bou-Harb, E., Crichigno, J., Kaddoum, G., & Ghani, N. (2019). Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-scale IoT Exploitations. IEEE Communications Surveys & Tutorials, 2702-2733. Parviainen, P., Tihinen, M., M., Kääriäinen, J., & Tep. (2017). Tackling the digitalization challenge: how to benefit from digitalization in practice. International journal of information systems and project management, 5(1), 63-67. Paudel, R., Muncy, T., & Eberle, W. (2019). Detecting DoS Attack in Smart Home IoT Devices Using a Graph-Based Approach. 2019 IEEE International Conference on Big Data (Big Data) (págs. 5249-5258). IEEE. Pokhrel, S., Abbas, R., & Aryal, B. (2021). IoT Security: Botnet detection in IoT using Machine learning. arXiv preprint arXiv:2104.02231. Reinsel, D., Gantz, J., & Rydning, J. (2018). Data age 2025: the digitization of the world from edge to core. (Paper Doc# US44413318), 1-29. Roopak, M., Tian, G. Y., & Chambers, J. (2019). Deep learning models for cyber security in IoT networks. IEEE 9th annual computing and communication workshop and conference (CCWC) (págs. 0452-0457). IEEE. Roopak, M., Tian, G. Y., & Chambers, J. (2020). An Intrusion Detection System Against DDoS Attacks in loT Networks. 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (págs. 0562-0567). IEEE. Shafiq, M., Tian, Z., Bashir, A. K., Du, X., & Guizani, M. (2020). IoT malicious traffic identification using wrapper-based feature selection mechanisms. Computers & Security, 101863. Smith, R., Palin, D., Ioulianou, P. P., Vassilakis, V. G., & Shahandashti, S. F. (2020). Battery draining attacks against edge computing nodes in IoT networks. Cyber- Physical Systems, 96-116. Statista Research. (2020). Internet of Things (IoT) active device connections installed base worldwide from 2015 to 2025. Obtenido de https://www.statista.com/statistics/1101442/iot-number-of-connected-devices- worldwide/ Stellios, I., Kotzanikolaou, P., Psarakis, M., Alcaraz, C., & Lopez, J. (2018). A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastructures and Services. IEEE Communications Surveys & Tutorials, 3453-3495. Susilo, B., & Sari, R. F. (2020). Intrusion Detection in IoT Networks Using Deep Learning Algorithm. Information, 11(5). Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition (págs. 2818-2826). IEEE. Tahsien, S. M., Karimipour, H., & Spachos, P. (2020). Machine learning based solutions for security of Internet of Things (IoT): A survey. Journal of Network and Computer Applications. Ujjan, R. M., Pervez, Z., Dahal, K., Bashir, A. K., Mumtaz, R., & González, J. (2020). Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN. Future Generation Computer Systems, 111, 763-779. Varga, P., Plosz, S., Soos, G., & Hegedus, C. (2017). Security threats and issues in automation IoT. 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS) (págs. 1-6). IEEE. Waheed, N., He, X., Ikram, M., Usman, M., Hashmi, S. S., & Usman, M. (2020). Security and Privacy in IoT Using Machine Learning and Blockchain: Threats and Countermeasures. ACM Computing Surveys (CSUR). Xiao, L., Wan, X., Lu, X., Zhang, Y., & Wu, D. (2018). IoT security techniques based on machine learning: How do IoT devices use AI to enhance security? IEEE Signal Processing Magazine, 41-49. Yang, Y., Wu, L., Yin, G., Li, L., & Zhao, H. (2017). A Survey on Security and Privacy Issues in Internet-of-Things. IEEE Internet of Things Journal, 1250-1258. 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dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
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dc.format.extent.spa.fl_str_mv	xii, 64 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Analítica
dc.publisher.department.spa.fl_str_mv	Departamento de la Computación y la Decisión
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gutiérrez Betancur, Sergio Armando576e9414f66eca5b522f30139d1d78c6Branch Bedoya, John Willian112eaa0bbeeaeb0d3d14dfe15d672a15Parra Jiménez, Jhon Alexander94c48523cb1c8496454a4909423f8205Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial2022-03-08T14:48:28Z2022-03-08T14:48:28Z2021-12-01https://repositorio.unal.edu.co/handle/unal/81148Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, tablasEl incremento en la implementación de dispositivos ciberfísicos conectados a internet en los últimos años los ha vuelto un foco de interés para posibles atacantes que ven una oportunidad para vulnerar sistemas que se componen de estos dispositivos como lo son los sistemas IoT. Debido a las condiciones particulares de dichos dispositivos como su capacidad de procesamiento, la duración de su batería, entre otros, se añade un reto adicional para poder proteger la información asociada a los sistemas IoT en términos de disponibilidad, confidencialidad e integridad. Por esto, en el presente trabajo de investigación se propone un método basado en la unión de redes neuronales convolucionales y redes neuronales recurrentes para la detección y clasificación de ataques de denegación de servicios en el contexto IoT. Para este método se transforman los flujos de datos a un formato de imágenes de tres canales, método que ha probado ser efectivo para la detección de ataques que comprometen la ciberseguridad. El modelo generado tiene resultados prometedores con una tasa de clasificación correcta de más del 99% en la detección de ataques y una tasa superior al 96% para la clasificación de los ataques abordados. (Texto tomado de la fuente)The increase in the adoption of cyber-physical devices connected to the Internet in recent years has made them a focus of interest for possible attackers who see an opportunity to violate systems that are made up with these devices, such as IoT systems. Due to the conditions of these devices such as their processing capacity, their battery life, among others, an additional challenge is added to be able to protect the information associated with IoT systems in terms of availability, confidentiality, and integrity. For this reason, in this research we propose a method based on the union of convolutional neural networks and recurrent neural networks for the detection and classification of denial-of-service attacks in the IoT context. For this method, the data flows are transformed into a three- channel image format, a method that has proven to be effective for detecting attacks that compromise cybersecurity. The generated model has promising results with an accuracy of more than 99% in the detection of attacks and a rate greater than 96% for the classification of the addressed attacks.MaestríaMagíster en Ingeniería - AnalíticaRedes neuronales artificiales, computación evolutiva, y reconocimiento de patronesÁrea Curricular de Ingeniería de Sistemas e Informáticaxii, 64 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaDepartamento de la Computación y la DecisiónFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónComputer Storage DevicesDispositivos de almacenamiento (Computadores)Firewalls (Computer science)Firewalls (Computadores)Internet de las CosasCiberseguridadAprendizaje ProfundoAmenazasRedes ConvolucionalesRedes RecurrentesIoTCibersecurityDeep LearningDoSConvolutional Neural NetworkRecurrent Neural NetworkUn método para la identificación y prevención temprana de incidentes de ciberseguridad en dispositivos del Internet de las CosasA Method Based on Deep Learning for the Early Detection and Characterization of Cybersecurity Incidents in Internet of Things DevicesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbbas, S., Faisal, M., Rahman, H. U., Khan, M. Z., Merabti, M., & Khan, A. U. (2018). Masquerading attacks detection in mobile ad hoc networks. IEEE Access, 55013- 55025.Alaba, F. A., Othman, M., Hashem, I. A., & Alotaibi, F. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications, 10-28.Al-Garadi, M. A., Mohamed, A., Al-Ali, A., Du, X., & Guizani, M. (2020). A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security. IEEE Communications Surveys & Tutorials.Alguliyev, R., Imamverdiyev, Y., & Sukhostat, L. (2018). Cyber-physical systems and their security issues. Computers in Industry, 100, 212-223.Amanullah, M. A., Habeeb, R. A., Nasaruddin, F. H., Gani, A., Ahmed, E., Nainar, A. S., . . . Imran, M. (2020). Deep learning and big data technologies for IoT security. Computer Communications.Anand, P., Singh, Y., Selwal, A., Alazab, M., Tanwar, S., & Kumar, N. (2020). IoT Vulnerability Assessment for Sustainable Computing: Threats, Current Solutions, and Open Challenges. IEEE Access, 168825-168853.Baig, Z. A., Sanguanpong, S., Firdous, S. N., Vo, V. N., Nguyen, T. G., & So-In, C. (2020). Averaged dependence estimators for DoS attack detection in IoT networks. Future Generation Computer Systems, 102, 198-209.Biswas, S., Sajal, M., Afrin, T., Bhuiyan, T., & Hassan, M. (2018). A study on remote code execution vulnerability in web applications. International Conference on Cyber Security and Computer Science (ICONCS 2018). Safranbolu.Bošnjak, L., Sreš, J., & Brumen, B. (2018). Brute-force and dictionary attack on hashed real-world passwords. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (págs. 1161-1166). IEEE.Brun, O., Yin, Y., Gelenbe, E., Kadioglu, Y. M., Augusto-Gonzalez, J., & Ramos, M. (2018). Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments. International ISCIS Security Workshop (págs. 79-89). Springer.Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2018). Network Intrusion Detection for IoT Security based on Learning Techniques. IEEE Communications Surveys & Tutorials, 2671-2701.Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (págs. 1251-1258). IEEE.Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N., & Qin, J. (2018). A survey on application of machine learning for Internet of Things. International Journal of Machine Learning and Cybernetics.Deogirikar, J., & Vidhate, A. (2017). Security attacks in IoT: A survey. 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), (págs. 32-37).Diro, A. A., & Chilamkurti, N. (2018). 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Institution of Engineering and Technology.Público generalORIGINAL1030660760.2021.pdf1030660760.2021.pdfTesis de Maestría en Ingeniería - Analíticaapplication/pdf1432719https://repositorio.unal.edu.co/bitstream/unal/81148/1/1030660760.2021.pdf30650c2ee436b56d338dc5ade829d519MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81148/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1030660760.2021.pdf.jpg1030660760.2021.pdf.jpgGenerated Thumbnailimage/jpeg5004https://repositorio.unal.edu.co/bitstream/unal/81148/3/1030660760.2021.pdf.jpg1cc2bff2813d0823bf1c6a8b2d8522bcMD53unal/81148oai:repositorio.unal.edu.co:unal/811482024-08-04 23:10:00.723Repositorio Institucional Universidad Nacional de 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

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