Abstract
We propose a method for application identification for network traffic by reservoir computing. Different from conventional approaches, the proposed method handles traffic flows as dynamical time series data and enables fast and real-time identification. We apply the proposed method to real traffic data and show that high identification accuracy is achieved. We also discuss an implementation as physical reservoirs based on optics and the impact of the proposed method to 5G networking.
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Acknowledgments
This work was supported by New Energy and Industrial Technology Development Organization (NEDO) under contract No. 18102284-0.
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Yamane, T., Héroux, J.B., Numata, H., Tanaka, G., Nakane, R., Hirose, A. (2019). Application Identification of Network Traffic by Reservoir Computing. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_41
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