MedibotDDoS

The MedibotDDoS dataset captures 200K+ traffic records with 13 features, representing benign and attack traffic against a medical syringe pump control system. It demonstrates how botnet-induced DDoS traffic disrupts healthcare devices, making it valuable for intrusion detection, anomaly detection, and medical device security research.

Context

  • The MedibotDDoS dataset was generated during an experimental evaluation of a botnet attack against a medical syringe pump control system.
  • The attack demonstrated how a DDoS (Distributed Denial of Service) could delay or halt the responsiveness of the control GUI, creating serious patient safety risks (e.g., multiple unintended “run” button presses leading to triple dosing).

Dataset Composition

  • Number of samples: 225,498 instances
  • Features: 13 network traffic features describing flow-level behavior

Feature Descriptions

  • SrcBytes – Number of bytes from source → destination
  • SrcLoad – Source → destination bits per second
  • DstBytes – Number of bytes from destination → source
  • DstLoad – Destination → source bits per second
  • DstPkts – Number of packets from destination → source
  • SrcRate – Packets per second from source → destination
  • DstRate – Packets per second from destination → source
  • Dur – Transaction duration (seconds)
  • TotPkts – Total transaction packets
  • TotBytes – Total transaction bytes
  • Load – Total transaction bits per second
  • Rate – Overall packets per second

Descriptive Statistics (Selected Highlights)

  • SrcBytes: mean 286.3, max 435,324
  • SrcPkts: mean 2.4, max 4,890
  • DstPkts: mean 0.94, max 2,411
  • DstBytes: mean 1052.6, max 12,858,140
  • Dur: mean 0.44 sec, max 5 sec
  • TotPkts: mean 3.38, max 5,979
  • TotBytes: mean 1,339, max 13,230,630
  • Load: mean 4,778,348 bps, max 15,168,000,000 bps
  • Rate: mean 2,535 pps, max 4,000,000 pps

Download Link

medibotddos.csv

Citation

Wei Lu. 2025. Web-based botnet for blocking control flow in open-source medical syringe pump. Int. J. Grid Util. Comput. 16, 2 (2025), 162–172. https://doi.org/10.1504/ijguc.2025.145175

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## Notes
- Access to some datasets may require completing a data use agreement.
- For questions, please contact Dr. Wei Lu at wlu@usnh.edu.

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