Problem of Network Traffic Classification in Multiprovider Cloud Infrastructures Based on Machine Learning Methods

Perepelkin, Dmitry; Ivanchikova, Maria

doi:10.1109/meco52532.2021.9460171

Cited by 2 publications

(1 citation statement)

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“…The increasingly stringent requirements on the responsiveness of smart services and the growing awareness of data protection and network security further exacerbate the above challenges. This is especially the case, considering that most existing self-supervised learning solutions are cloud-based in which every mobile device must upload its locally collected data samples to a cloud server and wait for feedback before making decisions [22], [23]. These solutions are known to suffer from long communication delay and risk of privacy leakage caused by showing raw data.…”

Section: Introductionmentioning

confidence: 99%

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-Supervised Learning Approach

Xiao

Xia

et al. 2024

IEEE Trans. on Mobile Comput.

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With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the *This work has been accepted at IEEE Transactions on Mobile Computing.

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Section: Introductionmentioning

confidence: 99%