Research on Identification of User Behavior by Stream Mining and Its Application
Project/Area Number |
25540034
|
Research Category |
Grant-in-Aid for Challenging Exploratory Research
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Allocation Type | Multi-year Fund |
Research Field |
Information network
|
Research Institution | Osaka City University |
Principal Investigator |
Ata Shingo 大阪市立大学, 大学院工学研究科, 教授 (30326251)
|
Project Period (FY) |
2013-04-01 – 2016-03-31
|
Project Status |
Completed (Fiscal Year 2015)
|
Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
|
Keywords | ユーザビヘイビア / アプリケーション識別 / トラヒック計測 / 機械学習 / ネットワーク / フロー / ビヘイビア / 分析 / ネットワーク制御 / ネットワーク計測 / パケットキャプチャ |
Outline of Final Research Achievements |
Recently, network traffic is becoming strongly biased by user's action taken in an application. In this research, we propose a method to infer such action (we define as "user behavior") from the monitored traffic. The proposed method firstly composes a set of traffic features (statistical features of measured traffic flows) and then applies a Supervised Machine Learning (ML) algorithm to identify the user behavior from the statistical features. Through experimental results by using actual traffic, we show that the proposed method achieves around 91% accuracy of identification for 9 major applications, and around 81% accuracy of identification for 43 user behaviors.
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Report
(4 results)
Research Products
(12 results)