Code for "Fingerprinting a flood"

For our report on the mid-2021 Monero transaction volume anomaly [1], analysis was performed in a Python-language Jupyter Notebook. An R program was also used to create one of the data sets. The source code for these programs is presented in this repository.

Requirements

Analysis Notebook

• Python 3 [2]

• Python libraries: see requirements.txt.

  Example installation command: pip install -r requirements.txt

• Jupyter Notebook [3].

• Data sets: download these CSV files of Monero blockchain data into the "csv" directory.

  Data set	Download location
  tx_attribute_2021.csv	Available from "Fingerprinting a flood" data repository [4]
  ringmember_height_flood.csv	Available from "Fingerprinting a flood" data repository [4]
  Noise-reduced-measure-of-youngest-ring-member.csv	Provided in this repository (see "csv" directory)

R program: Noise-reduced-measure-of-youngest-ring-member

• R [5]

• R libraries:

  • data.table
  • zoo
  • ggplot2

• Data sets: download these CSV files of Monero blockchain data into the "R" directory.

  Data set	Download location
  block_stat_2021.csv	Available from "Fingerprinting a flood" data repository [4]
  ringmember_height_flood.csv	Available from "Fingerprinting a flood" data repository [4]

Correspondence

isthmus (at) getmonero (dot) org

References

[1] Isthmus (Mitchell P. Krawiec-Thayer), Neptune, Rucknium, Jberman, Carrington - Fingerprinting a flood: forensic statistical analysis of the mid-2021 Monero transaction volume anomaly. https://mitchellpkt.medium.com/fingerprinting-a-flood-forensic-statistical-analysis-of-the-mid-2021-monero-transaction-volume-a19cbf41ce60

[2] Python. https://www.python.org

[3] Project Jupyter. https://jupyter.org

[4] GitHub - Neptune Research - Data from "Fingerprinting a flood". https://github.com/neptuneresearch/fingerprinting-a-flood-data

[5] R: The R Project for Statistical Computing. https://www.r-project.org