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Encrypted Video Traffic Trace Dataset (CSV)

Privacy-preserving CSV traces of self-collected encrypted video sessions from YouTube / Bilibili / Twitch, released for reproducible research on early identification of encrypted video traffic under resource constraints.

This dataset contains only:

  • packet direction (uplink/downlink relative to the client)
  • packet size (bytes)
  • relative timestamp (seconds since session start)

Not included: IP addresses, ports, MACs, payloads, URLs, SNI/Host, certificates, JA3/JA4, QUIC CIDs, or any other protocol handshake identifiers.

Notes

  • Every 50 sessions are saved into a single CSV file.

What’s inside

  • Platforms: YouTube / Bilibili / Twitch
  • Transport (in our captures):
    • YouTube: QUIC (UDP/HTTP3)
    • Bilibili: observed both (i) HTTPS over TCP with TLS 1.2 / TLS 1.3, and (ii) QUIC over UDP (HTTP/3) in some sessions
    • Twitch: TLS 1.3 (over TCP)
  • Capture setting: controlled 100 Mbps access rate, focusing on the first 30 seconds after video playback starts.

Header

The CSV contains two columns:

column type description
rel_ts_us int Relative packet arrival time in microseconds (µs) since the first packet of the session (rel_ts_us >= 0).
len int Total packet length in bytes.

Notes

  • rel_ts_us is relative time (not wall-clock time).
  • len is the total packet length in bytes (consistent with how packets were exported from the capture pipeline).

Repository structure

├── DataSet/
│   ├── YouTube/
│   │   ├── 1080/
│   │   ├── 720/
│   │   └── 480/
│   ├── Twitch/
│   │   ├── 1080/
│   │   ├── 720/
│   │   └── 480/
│   └── BiliBili/
│       ├── 1080/
│       ├── 720/
│       └── 480/
└── README.md

Notes

Due to repository size limits, we only upload a subset of the CSV files. If you need all CSV files or the original PCAP files, please contact feelingevening@gmail.com.

About

Privacy-preserving CSV traces of self-collected encrypted video sessions (YouTube/Bilibili/Twitch), containing only direction, packet size, and relative time, for early traffic identification research.

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