Identification of user behavior from flow statistics
Identification of user behavior from flow statistics
复制标题
从流量统计中识别用户行为
DOI：
10.1109/apnoms.2017.8094176
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Oka Ikuo
中科院分区：
文献类型：
--
作者：
Ata Shingo;Iemura Yusuke;Nakamura Nobuyuki;Oka Ikuo
最近，网络流量正变得强烈偏见的用户在应用程序中采取的行动。在本文中，我们提出了一种方法来推断这样的行动（我们定义为“用户行为”）从监测的流量。该方法首先合成一组交通特征（测量交通流的统计特征），然后应用监督机器学习（ML）算法从统计特征中识别用户行为。通过使用实际流量的实验结果，我们表明，该方法实现了约91%的识别准确率为9个主要的应用程序，约81%的识别准确率为43个用户行为。
Recently, network traffic is becoming strongly biased by user's action taken in an application. In this paper, 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.