Combining Communication Patterns & Traffic Patterns to Enhance Mobile Traffic Identification Performance

• Item type: Journal(1)
• 公開日: 2016-02-15
• タイトル: Combining Communication Patterns & Traffic Patterns to Enhance Mobile Traffic Identification Performance
• タイトル / 言語: en
• タイトル: Combining Communication Patterns & Traffic Patterns to Enhance Mobile Traffic Identification Performance
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：ネットワークサービスと分散処理] graphlet, mobile application, application identification, communication patterns, traffic classification, random forest
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Faculty of ICT, Mahidol University
• 著者所属: Faculty of ICT, Mahidol University
• 著者所属: National Institute of Informatics/Sokendai
• 著者所属(英): en
• 著者所属(英): Faculty of ICT, Mahidol University
• 著者所属(英): en
• 著者所属(英): Faculty of ICT, Mahidol University
• 著者所属(英): en
• 著者所属(英): National Institute of Informatics/Sokendai
• 著者名: Sophon, Mongkolluksamee; Vasaka, Visoottiviseth; Kensuke, Fukuda
• 著者名(英): Sophon, Mongkolluksamee; Vasaka, Visoottiviseth; Kensuke, Fukuda
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The bandwidth of a mobile network is limited and exhausted very fast with the huge number of mobile devices and applications. In order to manage and utilize the limited bandwidth, precise mobile application identification is required. In this work, the combination of communication patterns extracted from graphlet and traffic patterns represented by packet size distribution is studied for enhancing the performance of identifying mobile traffic. There are no privacy concerns for identifying traffic with our technique; it is also effective against the complexities of mobile traffic. The real traffic of five famous mobile applications (Facebook, Line, Skype, YouTube, and Web) is used in our evaluation. The identification performance is high (0.96) of F-measure even considering only a random 50 packets of traffic in a 3-minute duration. While identifying applications, the effect of other mixed background traffic is also studied and mitigated by filtering out short lived flows with a flow duration condition. The high identification performance is still maintained after this filtering process.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.2 (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The bandwidth of a mobile network is limited and exhausted very fast with the huge number of mobile devices and applications. In order to manage and utilize the limited bandwidth, precise mobile application identification is required. In this work, the combination of communication patterns extracted from graphlet and traffic patterns represented by packet size distribution is studied for enhancing the performance of identifying mobile traffic. There are no privacy concerns for identifying traffic with our technique; it is also effective against the complexities of mobile traffic. The real traffic of five famous mobile applications (Facebook, Line, Skype, YouTube, and Web) is used in our evaluation. The identification performance is high (0.96) of F-measure even considering only a random 50 packets of traffic in a 3-minute duration. While identifying applications, the effect of other mixed background traffic is also studied and mitigated by filtering out short lived flows with a flow duration condition. The high identification performance is still maintained after this filtering process.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.2 (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 57, 号 2, 発行日 2016-02-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764