Machine Learning based Video Hosting Site Identification Method for MVNO Networks
Anan Sawabe, Tomoki Ito, Takanori Iwai · 2020
Zero-rating service provided by Mobile Virtual Network Operators (MVNOs) has been attracting smartphone users who frequently watch web videos that are delivered by heavily bandwidth-consuming applications. With the increase of encrypted traffic, MVNOs need to identify video hosting sites accessed by smartphone users via encrypted traffic analysis for enabling such services. If traffic from permitted sites is identified as coming from non-permitted sites due to mistaken identification of video hosting sites, unreasonable payments are inevitable for MVNOs or subscribers, and vice versa. In this paper we propose two feature sets considering multiple flow transmission and analyze the feature sets by supervised machine learning for identifying video hosting sites accurately. The first set is traffic features extracted from flows for only transmitting video contents. The second is 4-tuple distribution of established flows for transmitting various contents in a single video web page. These feature sets are based on our investigation of the characteristics of four of the most popular video hosting sites in Japan. Through video hosting site identification experiments, the identification accuracy of single flow analysis reaches 85.9%, and the accuracy of the proposed method reaches 92.0%.