Número

Vol. 9 Núm. 3 (2021): Numero especial - XVI Jornadas Iberoamericanas de Ingeniería de Software e Ingeniería del Conocimiento - JIISIC 2021

Derechos de autor 2021 Investigación e Innovación en Ingenierías

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.

La revista Investigación e Innovación en Ingenierías respeta los derechos morales de cada autor, sin embargo los autores ceden los derechos patrimoniales de sus artículos, y al mismo tiempo certifican a través de una carta que su trabajo es inédito y no ha sido publicado anteriormente.

Arquitectura basada en tecnologías emergentes y tecnología de monitoreo de tráfico de red

https://doi.org/10.17081/invinno.9.3.5340
Juan José Caiza Narváez
Institución Universitaria Colegio Mayor del Cauca
https://orcid.org/0000-0002-8582-5946
Katerine Márceles Villalba
Institución Universitaria Colegio Mayor del Cauca
https://orcid.org/0000-0002-4571-0714
Siler Amador Donado
Universidad del Cauca, Colombia
https://orcid.org/0000-0002-4571-8273

Corresponding Author(s) : Katerine Márceles Villalba

kmarceles@unimayor.edu.co

Investigación e Innovación en Ingenierías, Vol. 9 Núm. 3 (2021): Numero especial - XVI Jornadas Iberoamericanas de Ingeniería de Software e Ingeniería del Conocimiento - JIISIC 2021

Resumen

Objetivo: Describir la estructuración de una arquitectura basada en ciberseguridad para mitigar el tráfico malicioso usando dispositivos IoT. Metodología: Para ello se utilizó una metodología basada en 4 fases, de las cuales, se da inicio con la identificación y selección de las tecnologías emergentes que presentan mayor impacto en la ciberseguridad de IoT, seguido de la identificación del algoritmo de inteligencia artificial que presente las características adecuadas a la estructura propuesta, más adelante se determinó la técnica de monitoreo adecuada para realizar la captura y monitorear el tráfico de red en tiempo real que circula por estos dispositivos IoT, para finalmente realizar la determinación de la arquitectura. Resultados: Así se obtuvo una arquitectura diseñada bajo tres capas, donde cada capa contiene la selección previa realizada en las respectivas fases. Conclusiones: Obteniendo un diseño que permite el monitoreo en tiempo real del tráfico que circula por los dispositivos IoT, considerando el almacenamiento de datos, que posteriormente se procesa bajo el algoritmo de IA(Inteligencia artificial) y determinará la creación de nuevas reglas para notificar posibles amenazas.

Palabras clave

Arquitectura, Ciberseguridad, Inteligencia Artificial, IoT, Tráfico de red
[1]
J. J. Caiza Narváez, K. Márceles Villalba, y S. Amador Donado, «Arquitectura basada en tecnologías emergentes y tecnología de monitoreo de tráfico de red », Investigación e Innovación en Ingenierías, vol. 9, n.º 3, pp. 18–31, dic. 2021.
Descargar cita
Endnote/Zotero/Mendeley (RIS)
BibTeX
Referencias
  • H. Tahaei, F. Afifi, A. Asemi, F. Zaki, and N. B. Anuar, “The rise of traffic classification in IoT networks: A survey,” Journal of Network and Computer Applications, vol. 154. Academic Press, p. 102538, Mar. 15, 2020, doi: 10.1016/j.jnca.2020.102538.
  • M. Aminu Lawal, R. Ahmed Shaikh, and S. Raheel Hassan, “An Anomaly Mitigation Framework for IoT Using Fog Computing,” mdpi.com, 2020, doi: 10.3390/electronics9101565.
  • H. HaddadPajouh, R. Khayami, A. Dehghantanha, K. K. R. Choo, and R. M. Parizi, “AI4SAFE-IoT: an AI-powered secure architecture for edge layer of Internet of things,” Neural Comput. Appl., vol. 32, no. 20, pp. 16119–16133, Oct. 2020, doi: 10.1007/s00521-020-04772-3.
  • S. K. Singh, S. Rathore, and J. H. Park, “BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence,” Futur. Gener. Comput. Syst., vol. 110, pp. 721–743, Sep. 2020, doi: 10.1016/j.future.2019.09.002.
  • A. Gómez-Cárdenas, X. Masip-Bruin, E. Marín-Tordera, and S. Kahvazadeh, “A novel and scalable naming strategy for IoT scenarios,” in Advances in Intelligent Systems and Computing, Nov. 2019, vol. 880, pp. 122–133, doi: 10.1007/978-3-030-02686-8_10.
  • M. Ahmad, M. B. Amin, S. Hussain, B. H. Kang, T. Cheong, and S. Lee, “Health Fog: a novel framework for health and wellness applications,” J. Supercomput., vol. 72, no. 10, pp. 3677–3695, Oct. 2016, doi: 10.1007/s11227-016-1634-x.
  • P. Empl and G. Pernul, “A flexible Security Analytics Service for the Industrial IoT; A flexible Security Analytics Service for the Industrial IoT,” vol. 10, 2021, doi: 10.1145/3445969.3450427.
  • A. R. Mathew and A. Al Hajj, “Secure Communications on IoT and Big Data,” Indian J. Sci. Technol., vol. 10, no. 11, 2017, doi: 10.17485/ijst/2017/v10i11/107974.
  • I. Farris, T. Taleb, Y. Khettab, and J. Song, “A survey on emerging SDN and NFV security mechanisms for IoT systems,” IEEE Commun. Surv. Tutorials, vol. 21, no. 1, pp. 812–837, Jan. 2019, doi: 10.1109/COMST.2018.2862350.
  • A. de Melo e Silva, J. J. C. Gondim, R. de Oliveira Albuquerque, and L. J. G. Villalba, “A methodology to evaluate standards and platforms within cyber threat intelligence,” Futur. Internet, vol. 12, no. 6, p. 108, Jun. 2020, doi: 10.3390/fi12060108.
  • S. Bauer, D. Fischer, C. Sauerwein, S. Latzel, D. Stelzer, and R. Breu, “Towards an Evaluation Framework for Threat Intelligence Sharing Platforms,” Hawaii Int. Conf. Syst. Sci. 2020, Jan. 2020, Accessed: Mar. 12, 2021. [Online]. Available: https://aisel.aisnet.org/hicss-53/dg/cybersecurity_and_government/3.
  • M. Kotpalliwar, R. W.-2015 F. I. Conference, and undefined 2015, “Classification of Attacks Using Support Vector Machine (SVM) on KDDCUP’99 IDS Database,” ieeexplore.ieee.org, 2015, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7280066/.
  • H. Saxena, V. R.-I. J. of C. Applications, and undefined 2014, “Intrusion detection in KDD99 dataset using SVM-PSO and feature reduction with information gain,” Citeseer, 2014, Accessed: Mar. 11, 2021. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.5101&rep=rep1&type=pdf.
  • M. Shakil Pervez and D. M. Farid, “Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs,” ieeexplore.ieee.org, 2015, doi: 10.1109/SKIMA.2014.7083539.
  • M. Yan and Z. Liu, “A new method of transductive SVM-based network intrusion detection,” in IFIP Advances in Information and Communication Technology, 2011, vol. 344 AICT, no. PART 1, pp. 87–95, doi: 10.1007/978-3-642-18333-1_12.
  • R. Kokila, … S. S.-2014 S. I., and undefined 2014, “DDoS detection and analysis in SDN-based environment using support vector machine classifier,” ieeexplore.ieee.org, 2015, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7229711/.
  • … A. C.-… on C. and and undefined 2014, “Confederation of fcm clustering, ann and svm techniques to implement hybrid nids using corrected kdd cup 99 dataset,” ieeexplore.ieee.org, 2014, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6949927/.
  • B. Rao and K. S. Science, “Fast kNN classifiers for network intrusion detection system,” sciresol.s3.us-east-2.amazonaws …, 2017, doi: 10.17485/ijst/2017/v10i14/93690.
  • S. A. A. AM Sharifi, “Intrusion detection based on joint of K-means and KNN - Google Académico,” 2015. https://scholar.google.es/scholar?hl=es&as_sdt=0%2C5&q=Intrusion+detection+based+on+joint+of+K-means+and+KNN&btnG= (accessed Mar. 11, 2021).
  • H. Shapoorifard and P. Shamsinejad, “Intrusion Detection using a Novel Hybrid Method Incorporating an Improved KNN,” 2017. Accessed: Mar. 11, 2021. [Online]. Available: https://fardapaper.ir/mohavaha/uploads/2018/08/Fardapaper-Intrusion-Detection-using-a-Novel-Hybrid-Method-Incorporating-an-Improved-KNN.pdf.
  • W. Meng, W. Li, and L.-F. Kwok, “Design of intelligent KNN-based alarm filter using knowledge-based alert verification in intrusion detection ,” Networks, vol. 8, no. 18, pp. 3883–3895, Dec. 2015, doi: 10.1002/sec.1307.
  • V. S. A. T. S Vishwakarma, “An intrusion detection system using KNN-ACO algorithm - Google Académico,” 2017. https://scholar.google.es/scholar?hl=es&as_sdt=0%2C5&q=An+intrusion+detection+system+using+KNN-ACO+algorithm&btnG= (accessed Mar. 11, 2021).
  • E. G. Dada, “A Hybridized SVM-kNN-pdAPSO Approach to Intrusion Detection System,” 2017. Accessed: Mar. 11, 2021. [Online]. Available: https://fardapaper.ir/mohavaha/uploads/2018/07/Fardapaper-A-Hybridized-SVM-kNN-pdAPSO-Approach-to-Intrusion-Detection-System.pdf.
  • B. Ingre, A. Yadav, and A. K. Soni, “Decision tree based intrusion detection system for NSL-KDD dataset,” in Smart Innovation, Systems and Technologies, 2018, vol. 84, pp. 207–218, doi: 10.1007/978-3-319-63645-0_23.
  • A. J. Malik and F. A. Khan, “A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection,” Cluster Comput., vol. 21, no. 1, pp. 667–680, Jun. 2018, doi: 10.1007/s10586-017-0971-8.
  • N. Relan, D. P.-2015 I. C. on, and undefined 2015, “Implementation of network intrusion detection system using variant of decision tree algorithm,” ieeexplore.ieee.org, 2015, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7029925/.
  • A. Akintola et al., “Gain Ratio and Decision Tree Classifier for Intrusion Detection,” Artic. Int. J. Comput. Appl., vol. 126, no. 1, pp. 975–8887, 2015, doi: 10.5120/ijca2015905983.
  • C. Azad and V. Kumar Jha, “Computer Network and Information Security,” Comput. Netw. Inf. Secur., vol. 8, pp. 56–71, 2015, doi: 10.5815/ijcnis.2015.08.07.
  • A. Balogun, A. O. & Balogun, and R. G. Jimoh, “Anomaly Intrusion Detection Using an Hybrid Of Decision Tree And K-Nearest Neighbor Recent Advances in data mining: Twitter mining View project Anomaly Intrusion Detection Using an Hybrid Of Decision Tree And K-Nearest Neighbor,” 2015. Accessed: Mar. 11, 2021. [Online]. Available: https://www.researchgate.net/publication/282326950.
  • A. A.-J. of C. and Communications and undefined 2015, “A decision tree classifier for intrusion detection priority tagging,” scirp.org, 2015, Accessed: Mar. 11, 2021. [Online]. Available: https://www.scirp.org/html/6-1730195_55717.htm.
  • D. Moon, H. Im, I. Kim, J. P.-T. J. of supercomputing, and undefined 2017, “DTB-IDS: an intrusion detection system based on decision tree using behavior analysis for preventing APT attacks,” Springer, 2017, Accessed: Mar. 11, 2021. [Online]. Available: https://link.springer.com/content/pdf/10.1007/s11227-015-1604-8.pdf.
  • Y. Ding, S. Chen, J. X.-2016 I. J. C. on, and undefined 2016, “Application of deep belief networks for opcode based malware detection,” ieeexplore.ieee.org, 2016, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7727705/.
  • M. Nadeem, O. Marshall, S. Singh, X. Fang, and X. Yuan, “Semi-Supervised Deep Neural Network for Network Intrusion Detection,” 2016. Accessed: Mar. 11, 2021. [Online]. Available: https://digitalcommons.kennesaw.edu/ccerphttps://digitalcommons.kennesaw.edu/ccerp/2016/Practice/2.
  • F. Qu, J. Zhang, Z. Shao, and S. Qi, “An intrusion detection model based on deep belief network,” in ACM International Conference Proceeding Series, Dec. 2017, pp. 97–101, doi: 10.1145/3171592.3171598.
  • M. Alom, … V. B.-2015 N. A., and undefined 2015, “Intrusion detection using deep belief networks,” ieeexplore.ieee.org, 2015, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7443094/.
  • Q. Tan, W. Huang, and Q. Li, “An intrusion detection method based on DBN in ad hoc networks,” Aug. 2016, pp. 477–485, doi: 10.1142/9789813140011_0056.
  • G. Zhao, C. Zhang, L. Z.-2017 I. International, and undefined 2017, “Intrusion detection using deep belief network and probabilistic neural network,” ieeexplore.ieee.org, 2017, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8005871/.
  • K. Alrawashdeh, C. P.-2016 15th I. international, and undefined 2016, “Toward an online anomaly intrusion detection system based on deep learning,” ieeexplore.ieee.org, 2016, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7838144/.
  • C. Yin, Y. Zhu, J. Fei, X. H.-I. Access, and undefined 2017, “A deep learning approach for intrusion detection using recurrent neural networks,” ieeexplore.ieee.org, 2017, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8066291/.
  • N. R. RB Krishnan, “‘An intellectual intrusion detection system model... - Google Académico,” 2016. https://scholar.google.es/scholar?hl=es&as_sdt=0%2C5&q=‘An+intellectual+intrusion+detection+system+model+for+attacks+classification+using+RNN&btnG= (accessed Mar. 11, 2021).
  • S. Althubiti, W. Nick, J. Mason, … X. Y.-S., and undefined 2018, “Applying long short-term memory recurrent neural network for intrusion detection,” ieeexplore.ieee.org, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8478898/.
  • J. Kim, J. Kim, H. Thu, H. K.-2016 I. Conference, and undefined 2016, “Long short term memory recurrent neural network classifier for intrusion detection,” ieeexplore.ieee.org, 2016, Accessed: Mar. 11, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7456805/.
  • G. Kim, H. Yi, J. Lee, Y. Paek, and S. Yoon, “LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems,” Nov. 2016, Accessed: Mar. 11, 2021. [Online]. Available: http://arxiv.org/abs/1611.01726.
  • A. M. Fred Agarap, “A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data ACM Reference Format: Abien Fred M. Agarap. 2018. A Neural Network Architecture Combin-ing Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data,” dl.acm.org, pp. 26–30, Feb. 2018, doi: 10.1145/3195106.3195117.
  • Y. Yu, J. Long, Z. C.-S. and C. Networks, and undefined 2017, “Network intrusion detection through stacking dilated convolutional autoencoders,” hindawi.com, 2017, Accessed: Mar. 11, 2021. [Online]. Available: https://www.hindawi.com/journals/scn/2017/4184196/abs/.
  • B. Kolosnjaji, A. Zarras, G. Webster, and C. Eckert, “Deep learning for classification of malware system call sequences,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9992 LNAI, pp. 137–149, doi: 10.1007/978-3-319-50127-7_11.
  • J. Saxe and K. Berlin, “EXpose: A character-level convolutional neural network with embeddings for detecting malicious URLs, file paths and registry keys,” arXiv. arXiv, Feb. 27, 2017.
  • X. Zeng, W. Wang, M. Zhu, X. Ye, and Y. Sheng, “Malware traffic classification using convolutional neural network for representation learning,” ieeexplore.ieee.org, 2017, doi: 10.1109/ICOIN.2017.7899588.
  • X. Zeng, W. Wang, M. Zhu, J. Wang, and Z. Yang, “End-to-end encrypted traffic classification with one-dimensional convolution neural networks,” ieeexplore.ieee.org, 2017, doi: 10.1109/ISI.2017.8004872.
  • B. Charyyev and M. H. Gunes, “IoT Event Classification Based on Network Traffic,” Aug. 2020, pp. 854–859, doi: 10.1109/infocomwkshps50562.2020.9162885.
  • Y. Wu, D. Wei, and J. Feng, “Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey,” Secur. Commun. Networks, vol. 2020, pp. 1–17, Aug. 2020, doi: 10.1155/2020/8872923.
Read More
Las biografías de los autores no están disponibles.
Descargar este archivo PDF
PDF XML