Abordagem Fuzzy Valorada Intervalarmente para Classificação de Tráfego de Streaming de Vídeo
Resumo
Este artigo contribui para a classificação do tráfego de streaming de vídeo explorando conceitos de Lógica Fuzzy Intervalar. Essa abordagem estende os trabalhos relacionados ao considerar as incertezas geradas pelas variações nas condições da rede e a imprecisão dos parâmetros que afetam o comportamento do fluxo da rede, o que aumenta a complexidade para alcançar maior acurácia na identificação do tráfego da rede. Algumas avaliações usando a abordagem de lógica intervalar para classificação de tráfego de streaming de vídeo são apresentadas com o uso de aplicações e datasets para validar a proposta.
Palavras-chave:
lógica fuzzy intervalar, streaming de vídeo, classificação de tráfego de rede
Referências
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Draper-Gil, G., Lashkari, A. H., et al. (2016). Characterization of encrypted and vpn traffic using timerelated. In Proc. of the 2nd ICISSP, pages 407–414.
Ducange, P., Mannarà, G., et al. (2017). A novel approach for internet traffic classification based on multiobjective evolutionary fuzzy classifiers. In 2017 FUZZ-IEEE, pages 1–6. IEEE.
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Hall, M. A. (1999). Correlation-based feature selection for machine learning.
Iglesias, F., Milosevic, J., and Zseby, T. (2019). Fuzzy classification boundaries against adversarial network attacks. Fuzzy Sets and Systems, 368:20–35.
Karnik, N. N. and Mendel, J. M. (1998). Introduction to type-2 fuzzy logic systems. In 1998 IEEE Int. Conf. on Fuzzy Systems Proc. IEEE World Congress on Computational Intelligence, volume 2, pages 915–920 vol.2.
Klement, E., Mesiar, R., and Pap, E. (2004). Triangular norms. position paper I: basic analytical and algebraic properties. Fuzzy Sets and Systems, 143(1):5–26.
Mendel, J. M. (2003). Fuzzy sets for words: a new beginning. In Fuzzy Systems, 2003. FUZZ ’03. The 12th IEEE Int. Conf. on, volume 1, pages 37–42.
Mendel, J. M. (2013). On km algorithms for solving type-2 fuzzy set problems. IEEE Transactions on Fuzzy Systems, 21(3):426–446.
Mendel, J. M., John, R. I., and Liu, F. (2006). Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Systems, 14(6):808–821.
Moura, B. M. P., Schneider, G. B., et al. (2019). Allocating virtual machines exploring type-2 fuzzy logic and admissible orders. In 2019 IEEE Int. Conf. Fuzzy Syst. (FUZZ-IEEE), pages 1–6.
Moustafa, N. and Slay, J. (2015). Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 Int. Conf. Mil. Commun. Inf. Syst. ICMCIS, pages 1–6.
Parfenov, D., Zabrodina, L., et al. (2020). Research of multiclass fuzzy classification of traffic for attacks identification in the networks. In J. Phys. Conf. Ser., volume 1679, page 042023. IOP Publishing.
Qader, K., Adda, M., and Al-Kasassbeh, M. (2017). Comparative analysis of clustering techniques in network traffic faults classification. Int. j. innov. res. comput. commun. eng., 5(4):6551–6563.
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Sani, Y., Mauthe, A., and Edwards, C. (2017). Adaptive bitrate selection: A survey. IEEE Communications Surveys & Tutorials, 19(4):2985–3014.
Shalaginov, A. and Franke, K. (2015). Automated generation of fuzzy rules from large-scale network traffic analysis in digital forensics investigations. In 2015 7th Int. Conf of Soft Computing and Pattern Recognition (SoCPaR), pages 31–36. IEEE.
Shifa, A., Asghar, M. N., et al. (2020). Fuzzy-logic threat classification for multi-level selective encryption over real-time video streams. J. Ambient Intell. Humaniz. Comput., 11(11):5369–5397.
Velan, P., Cermák, M., et al. (2015). A survey of methods for encrypted traffic classification and analysis. Int. Journal of Network Management, 25(5):355–374.
Wagner, C. (2013). Juzzy a java based toolkit for type-2 fuzzy logic. In 2013 IEEE Symp. on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ), pages 45–52.
Wu, D. and Nie, M. (2011). Comparison and practical implementation of type-reduction algorithms for type-2 fuzzy sets and systems. In FUZZ-IEEE, pages 2131–2138. IEEE.
Zadeh, L. (1975). The concept of a linguistic variable and its application to approximate reasoning—i. Information Sciences, 8(3):199 – 249.
Zapata, H., Bustince, H., et al. (2017). Interval-valued implications and interval-valued strong equality index with admissible orders. Int J Approx Reason, 88:91–109.
Al-Obeidat, F. and El-Alfy, E.-S. (2019). Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols. Pers Ubiquitous Comput, 23(5):777–791.
Asmuss, J. and Lauks, G. (2015). Network traffic classification for anomaly detection fuzzy clustering based approach. In 2015 12th (ICNC-FSKD), pages 313–318. IEEE.
Bentaleb, A., Taani, B., et al. (2018). A survey on bitrate adaptation schemes for streaming media over http. IEEE Commun. Surv. Tutor., 21(1):562–585.
Bujlow, T., Carela-Español, V., and Barlet-Ros, P. (2015). Independent comparison of popular DPI tools for traffic classification. Computer Networks, 76:75–89.
Bustince, H., Fernández, J., et al. (2013). Generation of linear orders for intervals by means of aggregation functions. Fuzzy Sets and Systems, 220:69–77.
Cho, K., Mitsuya, K., and Kato, A. (2000). Traffic data repository at the WIDE project. In Proc. of the Freenix Track: 2000 USENIX ATC, June 18-23, 2000, San Diego, CA, USA, pages 263–270. USENIX.
Claise, B., Sadasivan, G., et al. (2004). Rfc 3954: Cisco systems netflow services export version 9. IETF http://www.ietf.org/rfc/rfc3954.txt.
Draper-Gil, G., Lashkari, A. H., et al. (2016). Characterization of encrypted and vpn traffic using timerelated. In Proc. of the 2nd ICISSP, pages 407–414.
Ducange, P., Mannarà, G., et al. (2017). A novel approach for internet traffic classification based on multiobjective evolutionary fuzzy classifiers. In 2017 FUZZ-IEEE, pages 1–6. IEEE.
Gehrke, M., Walker, C., and Walker, E. (1996). Some comments on interval valued fuzzy sets. Int. Journal of Intelligent Systems, 11(10):751–759.
Hall, M. A. (1999). Correlation-based feature selection for machine learning.
Iglesias, F., Milosevic, J., and Zseby, T. (2019). Fuzzy classification boundaries against adversarial network attacks. Fuzzy Sets and Systems, 368:20–35.
Karnik, N. N. and Mendel, J. M. (1998). Introduction to type-2 fuzzy logic systems. In 1998 IEEE Int. Conf. on Fuzzy Systems Proc. IEEE World Congress on Computational Intelligence, volume 2, pages 915–920 vol.2.
Klement, E., Mesiar, R., and Pap, E. (2004). Triangular norms. position paper I: basic analytical and algebraic properties. Fuzzy Sets and Systems, 143(1):5–26.
Mendel, J. M. (2003). Fuzzy sets for words: a new beginning. In Fuzzy Systems, 2003. FUZZ ’03. The 12th IEEE Int. Conf. on, volume 1, pages 37–42.
Mendel, J. M. (2013). On km algorithms for solving type-2 fuzzy set problems. IEEE Transactions on Fuzzy Systems, 21(3):426–446.
Mendel, J. M., John, R. I., and Liu, F. (2006). Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Systems, 14(6):808–821.
Moura, B. M. P., Schneider, G. B., et al. (2019). Allocating virtual machines exploring type-2 fuzzy logic and admissible orders. In 2019 IEEE Int. Conf. Fuzzy Syst. (FUZZ-IEEE), pages 1–6.
Moustafa, N. and Slay, J. (2015). Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 Int. Conf. Mil. Commun. Inf. Syst. ICMCIS, pages 1–6.
Parfenov, D., Zabrodina, L., et al. (2020). Research of multiclass fuzzy classification of traffic for attacks identification in the networks. In J. Phys. Conf. Ser., volume 1679, page 042023. IOP Publishing.
Qader, K., Adda, M., and Al-Kasassbeh, M. (2017). Comparative analysis of clustering techniques in network traffic faults classification. Int. j. innov. res. comput. commun. eng., 5(4):6551–6563.
Sandvine (2020). The global internet phenomena report covid-19 spotlight.
Sani, Y., Mauthe, A., and Edwards, C. (2017). Adaptive bitrate selection: A survey. IEEE Communications Surveys & Tutorials, 19(4):2985–3014.
Shalaginov, A. and Franke, K. (2015). Automated generation of fuzzy rules from large-scale network traffic analysis in digital forensics investigations. In 2015 7th Int. Conf of Soft Computing and Pattern Recognition (SoCPaR), pages 31–36. IEEE.
Shifa, A., Asghar, M. N., et al. (2020). Fuzzy-logic threat classification for multi-level selective encryption over real-time video streams. J. Ambient Intell. Humaniz. Comput., 11(11):5369–5397.
Velan, P., Cermák, M., et al. (2015). A survey of methods for encrypted traffic classification and analysis. Int. Journal of Network Management, 25(5):355–374.
Wagner, C. (2013). Juzzy a java based toolkit for type-2 fuzzy logic. In 2013 IEEE Symp. on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ), pages 45–52.
Wu, D. and Nie, M. (2011). Comparison and practical implementation of type-reduction algorithms for type-2 fuzzy sets and systems. In FUZZ-IEEE, pages 2131–2138. IEEE.
Zadeh, L. (1975). The concept of a linguistic variable and its application to approximate reasoning—i. Information Sciences, 8(3):199 – 249.
Zapata, H., Bustince, H., et al. (2017). Interval-valued implications and interval-valued strong equality index with admissible orders. Int J Approx Reason, 88:91–109.
Publicado
31/07/2022
Como Citar
MONKS, Eduardo Maroñas; MOURA, Bruno; SCHENEIDER, Guilherme Bayer; YAMIN, Adenauer Correa; REISER, Renata Hax Sander; SANTOS, Helida.
Abordagem Fuzzy Valorada Intervalarmente para Classificação de Tráfego de Streaming de Vídeo. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 49. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 70-81.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2022.222827.
