Efficient Network Traffic Classification and Visualizing Abnormal Part Via Hybrid Deep Learning Approach : Xception + Bidirectional GRU

1
HanSeon Joo
HanSeon Joo
2
HaYoung Choi
HaYoung Choi
3
ChangHui Yun
ChangHui Yun
4
MinJong Cheon
MinJong Cheon

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GJCST Volume 21 Issue H3

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Efficient Network Traffic Classification and Visualizing Abnormal Part Via Hybrid Deep Learning Approach :  Xception + Bidirectional GRU Banner
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

HanSeon Joo. 2022. \u201cEfficient Network Traffic Classification and Visualizing Abnormal Part Via Hybrid Deep Learning Approach : Xception + Bidirectional GRU\u201d. Global Journal of Computer Science and Technology - H: Information & Technology GJCST-H Volume 21 (GJCST Volume 21 Issue H3): .

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Efficient network traffic classification & visualization in abnormal network scenarios using deep learning.
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
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GJCST-H Classification: C.2.1
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v1.2

Issue date

January 15, 2022

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English

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Efficient Network Traffic Classification and Visualizing Abnormal Part Via Hybrid Deep Learning Approach : Xception + Bidirectional GRU

HanSeon Joo
HanSeon Joo
HaYoung Choi
HaYoung Choi
ChangHui Yun
ChangHui Yun
MinJong Cheon
MinJong Cheon

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