VTIM: Video Title Identification Using Open Metadata

Abstract

Video-streaming applications are very popular these days. Existing studies of video streaming have attempted to identify video titles of users using machine learning techniques to identify specific patterns of video packets transmitted over the network. However, these studies have limitations when applied to actual environments where the network is congested or there are multiple users in the same network. This paper proposes Video Title Identification using open Metadata (VTIM), which identifies video titles by analyzing storyboards and Media Presentation Description (MPD) of MPEG-DASH in connection with video packets transmitted over the network. Attack was carried out using VTIM on 13,291 videos selected from actual video-streaming environment of YouTube. Our experiments show that VTIM is able to identify video titles with 100% accuracy at nearly thirty times faster than existing methods based on machine learning techniques. The paper also proposes and evaluates a countermeasure against VTIM.