Encrypted Traffic Identification by Fusing Softmax Classifier with Its Angular Margin Variant

Lin YAN; Mingyong ZENG; Shuai REN; Zhangkai LUO

doi:10.1587/transinf.2020EDL8130

Encrypted Traffic Identification by Fusing Softmax Classifier with Its Angular Margin Variant

Lin YAN, Mingyong ZENG, Shuai REN, Zhangkai LUO

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Summary :

Encrypted traffic identification is to predict traffic types of encrypted traffic. A deep residual convolution network is proposed for this task. The Softmax classifier is fused with its angular variant, which sets an angular margin to achieve better discrimination. The proposed method improves representation learning and reaches excellent results on the public dataset.

Publication: IEICE TRANSACTIONS on Information Vol.E104-D No.4 pp.517-520

Publication Date: 2021/04/01

Publicized: 2021/01/13

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2020EDL8130

Type of Manuscript: LETTER

Category: Information Network

Authors

Lin YAN
  State Key Laboratory of Mathematical Engineering and Advanced Computing
Mingyong ZENG
  State Key Laboratory of Mathematical Engineering and Advanced Computing
Shuai REN
  State Key Laboratory of Mathematical Engineering and Advanced Computing
Zhangkai LUO
  Space Engineering University

Keyword

traffic identification, convolution neural network, residual network, softmax classifier

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Lin YAN, Mingyong ZENG, Shuai REN, Zhangkai LUO, "Encrypted Traffic Identification by Fusing Softmax Classifier with Its Angular Margin Variant" in IEICE TRANSACTIONS on Information, vol. E104-D, no. 4, pp. 517-520, April 2021, doi: 10.1587/transinf.2020EDL8130.
Abstract: Encrypted traffic identification is to predict traffic types of encrypted traffic. A deep residual convolution network is proposed for this task. The Softmax classifier is fused with its angular variant, which sets an angular margin to achieve better discrimination. The proposed method improves representation learning and reaches excellent results on the public dataset.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8130/_p

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@ARTICLE{e104-d_4_517,
author={Lin YAN, Mingyong ZENG, Shuai REN, Zhangkai LUO, },
journal={IEICE TRANSACTIONS on Information},
title={Encrypted Traffic Identification by Fusing Softmax Classifier with Its Angular Margin Variant},
year={2021},
volume={E104-D},
number={4},
pages={517-520},
abstract={Encrypted traffic identification is to predict traffic types of encrypted traffic. A deep residual convolution network is proposed for this task. The Softmax classifier is fused with its angular variant, which sets an angular margin to achieve better discrimination. The proposed method improves representation learning and reaches excellent results on the public dataset.},
keywords={},
doi={10.1587/transinf.2020EDL8130},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Encrypted Traffic Identification by Fusing Softmax Classifier with Its Angular Margin Variant
T2 - IEICE TRANSACTIONS on Information
SP - 517
EP - 520
AU - Lin YAN
AU - Mingyong ZENG
AU - Shuai REN
AU - Zhangkai LUO
PY - 2021
DO - 10.1587/transinf.2020EDL8130
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2021
AB - Encrypted traffic identification is to predict traffic types of encrypted traffic. A deep residual convolution network is proposed for this task. The Softmax classifier is fused with its angular variant, which sets an angular margin to achieve better discrimination. The proposed method improves representation learning and reaches excellent results on the public dataset.
ER -