On the classification of mobile broadband applications

I. Ching Hsieh, Li Ping Tung, Bao-Shuh Lin 

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In the past decade, the Internet has been widely used in everyday life. Different types of mobile broadband applications are created and require an increasing amount of network resources. However, Internet service providers must maximize the use of these limited resources to provide users with different levels of quality of service. The first step toward traffic engineering is to perform traffic classification. In this work, we propose a classification method for identifying mobile applications executed at an early stage. We first collected traffic traces from a WiFi access point and then developed a hidden Markov model based on the packet size and transmission direction of the first 20 packets. In a series of our experiments, we evaluated the number of hidden states through 10-fold cross validation, and classified six types of mobile applications. The accuracy of the proposed method achieved 99.17%. In addition, we set specific threshold values for different application models and identified 91.33% of flows in the testing data set, which comprised unknown traffic flows. These experimental results demonstrate that our proposed method is effective for classifying Internet flows as well as unknown traffic in a real network.

Original languageEnglish
Title of host publication2016 IEEE 21st International Workshop on Computer Aided Modelling and Design of Communication Links and Networks, CAMAD 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages128-134
Number of pages7
ISBN (Electronic)9781509025589
DOIs
StatePublished - 16 Dec 2016
Event21st IEEE International Workshop on Computer Aided Modelling and Design of Communication Links and Networks, CAMAD 2016 - Toronto, Canada
Duration: 23 Oct 201625 Oct 2016

Publication series

NameIEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
ISSN (Electronic)2378-4873

Conference

Conference21st IEEE International Workshop on Computer Aided Modelling and Design of Communication Links and Networks, CAMAD 2016
Country/TerritoryCanada
CityToronto
Period23/10/1625/10/16

Keywords

  • Hidden Markov Model (HMM)
  • Mobile Application Classification
  • Traffic Identification

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