Presentation 2023-03-03
Real-time application identification method for mobile networks using machine learning
Tatsuhiro Ou, Akihiro Nakao,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) With the diversi?cation of mobile applications, the implementation of priority control that meets the communication requirements of each diversifying application and network slicing technology in 5G is awaited, and the need for Identifying the application names of traffic is increasing. This study introduces an efficient learning method by packet selection focusing on TCP control flags and a method to guarantee real-time performance by time limitations of packet collection for the purpose of implementing a real-time application identification system. We have implemented an automatic collection system for training data, present the effectiveness of our methodology and indicate a scheme for updating and operating future application identification systems.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Mobile App Identification / Machine Learning / Random Forest / SSL/TLS / Traffic Analysis
Paper # NS2022-232
Date of Issue 2023-02-23 (NS)

Conference Information
Committee IN / NS
Conference Date 2023/3/2(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Okinawa Convention Centre + Online
Topics (in Japanese) (See Japanese page)
Topics (in English) General
Chair Kunio Hato(Internet Multifeed) / Tetsuya Oishi(NTT)
Vice Chair Tsutomu Murase(Nagoya Univ.) / Takumi Miyoshi(Shibaura Insti of Tech.)
Secretary Tsutomu Murase(KDDI Research) / Takumi Miyoshi(Nagaoka Univ. of Tech.)
Assistant / Kotaro Mihara(NTT)

Paper Information
Registration To Technical Committee on Information Networks / Technical Committee on Network Systems
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Real-time application identification method for mobile networks using machine learning
Sub Title (in English)
Keyword(1) Mobile App Identification
Keyword(2) Machine Learning
Keyword(3) Random Forest
Keyword(4) SSL/TLS
Keyword(5) Traffic Analysis
1st Author's Name Tatsuhiro Ou
1st Author's Affiliation The University of Tokyo(Tokyo Univ.)
2nd Author's Name Akihiro Nakao
2nd Author's Affiliation The University of Tokyo(Tokyo Univ.)
Date 2023-03-03
Paper # NS2022-232
Volume (vol) vol.122
Number (no) NS-406
Page pp.pp.372-377(NS),
#Pages 6
Date of Issue 2023-02-23 (NS)