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A Distributed Fuzzy Support Vector Machines Model for Real Network Traffic

Abstract— Traffic classification has been widely applied for networking. Previous works paid little attention to robustness and massive data of practice network traffic. In this paper, we propose a new strategy for the Random Fuzzy Support Vector Machines(RA-FSVM) based on fuzzy membership, as well as employ genetic algorithm to find optimal parameters. Moreover, we introduce the distributed idea to structure clusters, so as to speed up RA-FSVM model’s computation. The results of simulation demonstrate the robustness and timeliness of RA-FSVM model.

Index Terms— Network traffic; Fuzzy Support Vector Machines; Genetic algorithm; Distributed computation

ZHAO Jihong
Xi’an University of Posts & Telecommunications, CHINA
JIANG Jie, QU Hua, ZHAO Jihong, ZHANG Yanpen
Xi’an Jiaotong University, CHINA

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Cite: JIANG Jie, QU Hua, ZHAO Jihong, ZHANG Yanpen, "A Distributed Fuzzy Support Vector Machines Model for Real Network Traffic," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, pp. 738-747, Hong Kong, 15-17 June, 2019.