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Recurrence Plots-Based Network Attack Classification Using CNN-Autoencoders

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

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Abstract

The advent of the Internet of Things, with the consequent changes in network architectures and communication dynamics, has affected the security market by introducing further complexity in traffic flow analysis, classification, and detection activities. Consequently, to face these emerging challenges, new empowered strategies are needed to effectively spot anomalous events within legitimate traffic and guarantee the success of early alerting facilities. However, such detection and classification strategies strongly depend on the right choice of employed features, which can be mined from individual or aggregated observations. Therefore, this work explores the theory of dynamic non-linear systems for effectively capturing and understanding the more expressive Internet traffic dynamics arranged as Recurrence Plots. To accomplish this, it leverages the abilities of Convolutional Autoencoders to derive meaningful features from the constructed plots. The achieved results, derived from a real dataset, demonstrate the effectiveness of the presented approach by also outperforming state-of-the-art classifiers.

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Notes

  1. 1.

    https://www.unb.ca/cic/datasets/ids-2017.html.

References

  1. Palmieri, F.: Network anomaly detection based on logistic regression of nonlinear chaotic invariants. J. Netw. Comput. Appl. 148, 102460 (2019). ISSN 1084–8045. https://doi.org/10.1016/j.jnca.2019.102460

  2. Poincaré, H.: Sur le problème des trois corps et les équations de la dynamique. Acta mathematica 13(1), A3–A270 (1890)

    MathSciNet  Google Scholar 

  3. Eckmann, J.-P., Kamphorst, S.O., Ruelle, D.: Europhysics Letters (EPL). https://doi.org/10.1209/0295-5075/4/9/004 (1987)

  4. Marwan, N., Carmen Romano, M., Thiel, M., Kurths, J.: Recurrence plots for the analysis of complex systems. Phys. Rep. 438(5), 237–329 (2007). ISSN 0370–1573. https://doi.org/10.1016/j.physrep.2006.11.001

  5. Takens, F.: Detecting strange attractors in turbulence. In: David, R., Young, L.S., (eds.) Dynamical Systems and Turbulence, Warwick 1980, pp. 366–381, Berlin, Heidelberg, 1981. Springer, Berlin Heidelberg (1980). ISBN 978-3-540-38945-3

    Google Scholar 

  6. Garcia-Ceja, E., Uddin, M.Z., Torresen, J.: Classification of recurrence pots distance matrices with a convolutional neural network for activity recognition. Procedia Comput. Sci. 130, 157–163 (2018)

    Article  Google Scholar 

  7. Menini, N., et al.: A soft computing framework for image classification based on recurrence plots. IEEE Geosci. Rem. Sens. Lett. 16(2), 320–324 (2019). https://doi.org/10.1109/LGRS.2018.2872132

    Article  Google Scholar 

  8. Patil, M.S., Loka, R., Parimi, A.M.: Application of ARIMA and 2d-CNNNS using recurrence plots for medium-term load forecasting. In: 2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE), pp. 1–5 (2021). https://doi.org/10.1109/CIYCEE53554.2021.9676838

  9. Kirichenko, L., Radivilova, T., Bulakh, V., Zinchenko, P., Saif Alghawli, A.: Two approaches to machine learning classification of time series based on recurrence plots. In: 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), pp. 84–89 (2020). https://doi.org/10.1109/DSMP47368.2020.9204021

  10. Andrzejak, R.G., et al.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E 64, 061907 (2001)

    Article  Google Scholar 

  11. Tziridis, K., Kalampokas, T., Papakostas, G.A.: EEG signal analysis for seizure detection using recurrence plots and tchebichef moments. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0184–0190 (2021). https://doi.org/10.1109/CCWC51732.2021.9376134

  12. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q., (eds.), Advances in Neural Information Processing Systems. Curran Associates Inc

    Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2015). https://arxiv.org/abs/1512.03385

  14. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2016). https://arxiv.org/abs/1608.06993

  15. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). https://arxiv.org/abs/1409.1556

  16. Sartoli, S., Wei, Y., Hampton, S.: Malware classification using recurrence plots and deep neural network. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 901–906 (2020).https://doi.org/10.1109/ICMLA51294.2020.00147

  17. Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E., Ahmadi, M.: Microsoft malware classification challenge (2018). https://arxiv.org/abs/1802.10135

  18. Palmieri, F., Fiore, U.: A nonlinear, recurrence-based approach to traffic classification. Comput. Netw. 53(6), 761–773 (2009). https://doi.org/10.1016/j.comnet.2008.12.015

    Article  MATH  Google Scholar 

  19. Hirata, Y.: Recurrence plots for characterizing random dynamical systems. Commun. Nonlinear Sci. Numer. Simul. 94, 105552 (2021). https://doi.org/10.1016/j.cnsns.2020.105552. ISSN 1007–5704

  20. Palmieri, F., Fiore, U.: Network anomaly detection through nonlinear analysis. Comp. Secur. 29(7), 737–755 (2010)

    Article  Google Scholar 

  21. Hu, Min, Feng, Xiaowei, Zhou, Shengchen, Xu, Wei: Anomaly detection in time series data based on unthresholded recurrence plots. In: Abawajy, Jemal, Choo, Kim-Kwang Raymond., Islam, Rafiqul, Xu, Zheng, Atiquzzaman, Mohammed (eds.) ATCI 2018. AISC, vol. 842, pp. 477–484. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98776-7_52

    Chapter  Google Scholar 

  22. Wang, P., Ye, F., Chen, X., Qian, Y.: Datanet: deep learning based encrypted network traffic classification in SDN home gateway. IEEE Access 6, 55380–55391 (2018). https://doi.org/10.1109/ACCESS.2018.2872430

    Article  Google Scholar 

  23. D’Angelo, G., Palmieri, F.: Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction. J. Netw. Comput. Appl. 173, 102890 (2021). https://doi.org/10.1016/j.jnca.2020.102890. ISSN 1084–8045

  24. Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., Lloret, J.: Network traffic classifier with convolutional and recurrent neural networks for internet of things. IEEE Access 5, 18042–18050 (2017)

    Article  Google Scholar 

  25. Lotfollahi, M., Jafari Siavoshani, M., Shirali Hossein Zade, R., Saberian, M.: Deep packet: a novel approach for encrypted traffic classification using deep learning. Soft Comput. 24(3), 1999–2012 (2020).https://doi.org/10.48550/arXiv.1709.02656

  26. Zbilut, J.P., Webber, C.L.: Embeddings and delays as derived from quantification of recurrence plots. Phys. Lett. A 3, 199–203 (1992). https://doi.org/10.1016/0375-9601(92)90426-M. ISSN 0375–9601

  27. Almeida-\(\tilde{\rm N}\)auñay, A.F., Benito, R.M., Quemada, M., Losada, J.C., Tarquis, A.M.: Recurrence plots for quantifying the vegetation indices dynamics in a semi-arid grassland. Geoderma 406, 115488 (2022). https://doi.org/10.1016/j.geoderma.2021.115488. ISSN 0016–7061

  28. Hegger, R., Kantz, H., Schreiber, T.: Practical implementation of nonlinear time series methods: the tisean package. Chaos Interdisc. J. Nonlinear Sci. 9(2), 413–435 (1999)

    Google Scholar 

  29. Kennel, M.B., Brown, R., Abarbanel, H.D.I.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45, 3403–3411 (1992)

    Article  Google Scholar 

  30. Makhzani, A., Frey, B.: k-sparse autoencoders (2014). arXiv: 1312.5663 [cs.LG]

  31. Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP, vol. 1, pp. 108–116 (2018)

    Google Scholar 

  32. Mannattil, M.: NoLiTSA - NonLinear Time Series Analysis (2022). https://github.com/manu-mannattil/nolitsa. Accessed 26 July 2022

  33. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques 4th. isbn: 0128042915 (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2016)

    Google Scholar 

  34. Luo, W., Li, J., Yang, J., Xu, W., Zhang, J.: Convolutional sparse autoencoders for image classification. IEEE Trans. Neural Netw. Learn. Syst. 29(7), 3289–3294 (2018). https://doi.org/10.1109/TNNLS.2017.2712793

    Article  MathSciNet  Google Scholar 

  35. Marulli, F., Balzanella, A., Campanile, L., Iacono, M., Mastroianni, M.: Exploring a federated learning approach to enhance authorship attribution of misleading information from heterogeneous sources. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2021). https://doi.org/10.1109/IJCNN52387.2021.9534377

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Acknowledgements

This work was partially supported by project SERICS (PE00000014) under the NRRP MUR program funded by the EU - NGEU.

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Authors and Affiliations

  1. Department of Computer Science, Unversity of Salerno, Fisciano, (SA), Italy

    Gianni D’Angelo, Eslam Farsimadan & Francesco Palmieri

Authors
  1. Gianni D’Angelo
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  2. Eslam Farsimadan
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  3. Francesco Palmieri
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Corresponding author

Correspondence to Eslam Farsimadan .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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D’Angelo, G., Farsimadan, E., Palmieri, F. (2023). Recurrence Plots-Based Network Attack Classification Using CNN-Autoencoders. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14105. Springer, Cham. https://doi.org/10.1007/978-3-031-37108-0_13

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  • DOI: https://doi.org/10.1007/978-3-031-37108-0_13

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