Accurate Identification of Internet Video Traffic Using Byte Code Distribution Features

Xie, Yachen; Deng, Hanbo; Peng, Lizhi; Chen, Zhenxiang

doi:10.1007/978-3-030-05051-1_4

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…Xie et al [12] attempted to analyze video scenes and unique traffic patterns when quick-scene action movies were played. Based on the two video themes of romance and action movies, video packets were grouped according to size and a traffic feature called the byte code distribution was created.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 2 shows a typical ON-OFF pattern when the client receives video segments from the DASH server. Earlier studies [9]- [12] identify video titles using machine learning techniques such as SVM, BPNN and C4.5 Decision Tree to such traffic patterns with a network protocol analyzer such as Wireshark [34]. However, these approaches have the following limitations:…”

Section: E Limitations Of the Related Work On Using Traffic Patternsmentioning

confidence: 99%

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VTIM: Video Title Identification Using Open Metadata

et al. 2020

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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.

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Section: Related Workmentioning

confidence: 99%

Section: E Limitations Of the Related Work On Using Traffic Patternsmentioning

confidence: 99%