Abstract
The rapid development of network services not only expands the scale of Internet traffic, but also diversifies the types of traffic. In this work, we design a light-weight compromise scheme to meet the management requirements of large-scale and business sensitive scenarios. The proposed scheme regards the mixed traffic as a whole and directly analyzes the component structure for it. It converts the structural and attribute features into a traffic profile by encoding, embedding and mapping. Then the traffic profile is used to infer the component structure based on CNN. The proposed scheme has no need to perform flow-by-flow classification, it is not limited to the “quantity” balance of traffic, but also considers the types of traffic in each link. Based on the experiments with actual dataset, the results show that the proposed scheme can infer component structure for mixed traffic quickly and accurately.
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Funding
This work was supported by the Natural Science Foundation of China (No. 61972431,U2001204,61873290), and the Natural Science Foundation of Guangdong Province, China (No.2018A030313303).
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Wu, Z., Xie, Y., Wu, Z. (2022). A Light-Weight Scheme for Detecting Component Structure of Network Traffic. In: Shen, H., et al. Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science(), vol 13148. Springer, Cham. https://doi.org/10.1007/978-3-030-96772-7_3
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DOI: https://doi.org/10.1007/978-3-030-96772-7_3
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