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Encrypted Traffic Categorization Based on Flow Byte Sequence Convolution Aggregation Network

Lin YAN, Mingyong ZENG, Shuai REN, Zhangkai LUO

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Summary :

Traffic categorization aims to classify network traffic into major service types. A modern deep neural network based on temporal sequence modeling is proposed for encrypted traffic categorization. The contemporary techniques such as dilated convolution and residual connection are adopted as the basic building block. The raw traffic files are pre-processed to generate 1-dimensional flow byte sequences and are feed into our specially-devised network. The proposed approach outperforms other existing methods greatly on a public traffic dataset.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.7 pp.996-999
Publication Date
2021/07/01
Publicized
2020/12/24
Online ISSN
1745-1337
DOI
10.1587/transfun.2020EAL2102
Type of Manuscript
LETTER
Category
Mobile Information Network and Personal Communications

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