Mobile Application Identification based on Hidden Markov Model
With the increasing number of mobile applications, there has more challenging network management tasks to resolve. Users also face security issues of the mobile Internet application when enjoying the mobile network resources. Identifying applications that correspond to network traffic can help netwo...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2018-01-01
|
Series: | ITM Web of Conferences |
Online Access: | https://doi.org/10.1051/itmconf/20181702002 |
id |
doaj-9ea8a72f2c214ada9a46649d6e98004d |
---|---|
record_format |
Article |
spelling |
doaj-9ea8a72f2c214ada9a46649d6e98004d2021-02-02T07:28:44ZengEDP SciencesITM Web of Conferences2271-20972018-01-01170200210.1051/itmconf/20181702002itmconf_wcsn2018_02002Mobile Application Identification based on Hidden Markov ModelYang XinyanYi YunhuiXiao XinguangMeng YanhongWith the increasing number of mobile applications, there has more challenging network management tasks to resolve. Users also face security issues of the mobile Internet application when enjoying the mobile network resources. Identifying applications that correspond to network traffic can help network operators effectively perform network management. The existing mobile application recognition technology presents new challenges in extensibility and applications with encryption protocols. For the existing mobile application recognition technology, there are two problems, they can not recognize the application which using the encryption protocol and their scalability is poor. In this paper, a mobile application identification method based on Hidden Markov Model(HMM) is proposed to extract the defined statistical characteristics from different network flows generated when each application starting. According to the time information of different network flows to get the corresponding time series, and then for each application to be identified separately to establish the corresponding HMM model. Then, we use 10 common applications to test the method proposed in this paper. The test results show that the mobile application recognition method proposed in this paper has a high accuracy and good generalization ability.https://doi.org/10.1051/itmconf/20181702002 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yang Xinyan Yi Yunhui Xiao Xinguang Meng Yanhong |
spellingShingle |
Yang Xinyan Yi Yunhui Xiao Xinguang Meng Yanhong Mobile Application Identification based on Hidden Markov Model ITM Web of Conferences |
author_facet |
Yang Xinyan Yi Yunhui Xiao Xinguang Meng Yanhong |
author_sort |
Yang Xinyan |
title |
Mobile Application Identification based on Hidden Markov Model |
title_short |
Mobile Application Identification based on Hidden Markov Model |
title_full |
Mobile Application Identification based on Hidden Markov Model |
title_fullStr |
Mobile Application Identification based on Hidden Markov Model |
title_full_unstemmed |
Mobile Application Identification based on Hidden Markov Model |
title_sort |
mobile application identification based on hidden markov model |
publisher |
EDP Sciences |
series |
ITM Web of Conferences |
issn |
2271-2097 |
publishDate |
2018-01-01 |
description |
With the increasing number of mobile applications, there has more challenging network management tasks to resolve. Users also face security issues of the mobile Internet application when enjoying the mobile network resources. Identifying applications that correspond to network traffic can help network operators effectively perform network management. The existing mobile application recognition technology presents new challenges in extensibility and applications with encryption protocols. For the existing mobile application recognition technology, there are two problems, they can not recognize the application which using the encryption protocol and their scalability is poor. In this paper, a mobile application identification method based on Hidden Markov Model(HMM) is proposed to extract the defined statistical characteristics from different network flows generated when each application starting. According to the time information of different network flows to get the corresponding time series, and then for each application to be identified separately to establish the corresponding HMM model. Then, we use 10 common applications to test the method proposed in this paper. The test results show that the mobile application recognition method proposed in this paper has a high accuracy and good generalization ability. |
url |
https://doi.org/10.1051/itmconf/20181702002 |
work_keys_str_mv |
AT yangxinyan mobileapplicationidentificationbasedonhiddenmarkovmodel AT yiyunhui mobileapplicationidentificationbasedonhiddenmarkovmodel AT xiaoxinguang mobileapplicationidentificationbasedonhiddenmarkovmodel AT mengyanhong mobileapplicationidentificationbasedonhiddenmarkovmodel |
_version_ |
1724299349175828480 |