| 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)
|
|---|
| 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.
|
