Análise Exploratória de Dados do StreamDataNetClass
Resumo
A classificação de tráfego de rede tem recebido maior atenção das comunidades tecnológica e acadêmica à medida que o número de dispositivos e usuários cresce. Considerando o campo da inteligência artificial (IA), é extremamente importante a existência de conjuntos de dados valiosos e consistentes. Este estudo apresenta uma Análise Exploratória de Dados (AED), utilizando a linguagem de programação Python, em um conjunto de dados criado para o problema de classificação de tráfego de streaming em rede, o StreamDataNetClass.
Palavras-chave:
análise exploratória de dados, classificação de dados, tráfego de rede
Referências
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Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4):83–93. DOI: 10.1109/2.53
Zhang, H., Ding, Y., Niu, J., and Jung, S. (2024). How artificial intelligence affects international industrial transfer—evidence from industrial robot application. Journal of Asian Economics, 95:101815. DOI: 10.1016/j.asieco.2024.101815
Draper-Gil, G., Lashkari, A. H., Mamun, M. S. I., and A. Ghorbani, A. (2016). Characterization of encrypted and vpn traffic using time-related features. In Proceedings of the 2nd International Conference on Information Systems Security and Privacy - ICISSP, pages 407–414. INSTICC, SciTePress. DOI: 10.5220/0005740704070414
Fu, W. (2024). Exploratory data analysis and machine learning models for stroke prediction. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - DAML, pages 211–217. INSTICC, SciTePress. DOI: 10.5220/0012783300003885
Hattak, A., Iadarola, G., Martinelli, F., Mercaldo, F., and Santone, A. (2023). A method for robust and explainable image-based network traffic classification with deep learning. In Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT, pages 385–393. INSTICC, SciTePress. DOI: 10.5220/0012083200003555
Komorowski, M., Marshall, D. C., Salciccioli, J. D., and Crutain, Y. (2016). Exploratory data analysis. Secondary analysis of electronic health records, pages 185–203. DOI: 10.1007/978-3-319-43742-2_15
Li, H., Xue, T., Zhang, A., Luo, X., Kong, L., and Huang, G. (2024). The application and impact of artificial intelligence technology in graphic design: A critical interpretive synthesis. Heliyon. DOI: 10.1016/j.heliyon.2024.e40037
M. De Resende, A. A., D. De Melo, P. H. A., Souza, J. R., Cattelan, R. G., and Miani, R. S. (2022). Traffic classification of home network devices using supervised learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, pages 114–120. INSTICC, SciTePress. DOI: 10.5220/0010785500003116
Monks, E. M. (2023). Abordagem Híbrida FuzzyNetClass: Uma Contribuição à Classificação do Tráfego de Streaming de Vídeo Integrando Lógica Fuzzy Valorada Intervalarmente e Aprendizagem de Máquina. Phd tese, Universidade Federal de Pelotas, Pelotas, RS.
Monks, E. M., Moura, B., Scheneider, G. B., Yamin, A. C., Reiser, R. H. S., and Santos, H. (2022). Abordagem fuzzy valorada intervalarmente para classificação de tráfego de streaming de vídeo. In Anais do XLIX Seminário Integrado de Software e Hardware, pages 70–81. SBC. DOI: 10.5753/semish.2022.222827
Nelson, L. S. (1998). The anderson-darling test for normality. Journal of Quality Technology, 30(3):298–299. DOI: 10.1080/00224065.1998.11979858
Qiu, T., Chen, N., Li, K., Atiquzzaman, M., and Zhao, W. (2018). How can heterogeneous internet of things build our future: A survey. IEEE Communications Surveys & Tutorials, 20(3):2011–2027. DOI: 10.1109/COMST.2018.2803740
Rao, A., Legout, A., Lim, Y.-s., Towsley, D., Barakat, C., and Dabbous, W. (2011). Network characteristics of video streaming traffic. In Proceedings of the Seventh COnference on Emerging Networking EXperiments and Technologies, CoNEXT ’11, New York, NY, USA. Association for Computing Machinery.
Raparthi, M., Gayam, S. R., Kasaraneni, B. P., Kondapaka, K. K., Putha, S., Pattyam, S. P., Thuniki, P., Kuna, S. S., Nimmagadda, V. S. P., and Sahu, M. K. (2024). Exploratory data analysis techniques-a comprehensive review: Reviewing various exploratory data analysis techniques and their applications in uncovering insights from raw data. Australian Journal of Machine Learning Research & Applications, 4(1):215–225.
Sahoo, K., Samal, A. K., Pramanik, J., and Pani, S. K. (2019). Exploratory data analysis using python. International Journal of Innovative Technology and Exploring Engineering, 8(12):4727–4735. DOI: 10.35940/ijitee.L3591.1081219
Salman, O., Elhajj, I. H., Kayssi, A., and Chehab, A. (2020). A review on machine learning–based approaches for internet traffic classification. Annals of Telecommunications, 75(11):673–710. DOI: 10.1007/s12243-020-00770-7
Spearman, C. (1961). The proof and measurement of association between two things. The American journal of psychology. DOI: 10.1037/11491-005
Ventocilla, E. and Riveiro, M. (2019). Visual growing neural gas for exploratory data analysis. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - IVAPP, pages 58–71. INSTICC, SciTePress. DOI: 10.5220/0007364000580071
Wang, C., Li, T., Lu, Z., Wang, Z., Alballa, T., Alhabeeb, S. A., Albely, M. S., and Khalifa, H. A. E.-W. (2025). Application of artificial intelligence for feature engineering in education sector and learning science. Alexandria Engineering Journal, 110:108–115. DOI: 10.1016/j.aej.2024.09.100
Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4):83–93. DOI: 10.1109/2.53
Zhang, H., Ding, Y., Niu, J., and Jung, S. (2024). How artificial intelligence affects international industrial transfer—evidence from industrial robot application. Journal of Asian Economics, 95:101815. DOI: 10.1016/j.asieco.2024.101815
Publicado
10/09/2025
Como Citar
SILVA, Gabriel et al.
Análise Exploratória de Dados do StreamDataNetClass. In: WORKSHOP-ESCOLA DE INFORMÁTICA TEÓRICA (WEIT), 8. , 2025, Ponta Grossa/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 102-110.
