blockprint

This is a repository for discussion and development of tools for Ethereum block fingerprinting.

The primary aim is to measure beacon chain client diversity using on-chain data, as described in this tweet:

https://twitter.com/sproulM_/status/1440512518242197516

The latest estimate using the improved k-NN classifier for slots 2048001 to 2164916 is:

Getting Started

The raw data for block fingerprinting needs to be sourced from Lighthouse's block_rewards API.

This is a new API that is currently only available on the block-rewards-api branch, i.e. this pull request: https://github.com/sigp/lighthouse/pull/2628

Lighthouse can be built from source by following the instructions here.

VirtualEnv

All Python commands should be run from a virtualenv with the dependencies from requirements.txt installed.

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

k-NN Classifier

The best classifier implemented so far is a k-nearest neighbours classifier in knn_classifier.py.

It requires a directory of structered training data to run, and can be used either via a small API server, or in batch mode.

You can download a large (886M) training data set here.

To run in batch mode against a directory of JSON batches (individual files downloaded from LH), use this command:

./knn_classifier.py training_data_proc data_to_classify

Expected output is:

classifier score: 0.9886800869904645
classifying rewards from file slot_2048001_to_2050048.json
total blocks processed: 2032
Lighthouse,0.2072
Nimbus or Prysm,0.002
Nimbus or Teku,0.0025
Prysm,0.6339
Prysm or Teku,0.0241
Teku,0.1304

Training the Classifier

The classifier is trained from a directory of reward batches. You can fetch batches with the load_blocks.py script by providing a start slot, end slot and output directory:

./load_blocks.py 2048001 2048032 testdata

The directory testdata now contains 1 or more files of the form slot_X_to_Y.json downloaded from Lighthouse.

To train the classifier on this data, use the prepare_training_data.py script:

./prepare_training_data.py testdata testdata_proc

This will read files from testdata and write the graffiti-classified training data to testdata_proc, which is structured as directories of single block reward files for each client.

$ tree testdata_proc
testdata_proc
├── Lighthouse
│   ├── 0x03ae60212c73bc2d09dd3a7269f042782ab0c7a64e8202c316cbcaf62f42b942.json
│   └── 0x5e0872a64ea6165e87bc7e698795cb3928484e01ffdb49ebaa5b95e20bdb392c.json
├── Nimbus
│   └── 0x0a90585b2a2572305db37ef332cb3cbb768eba08ad1396f82b795876359fc8fb.json
├── Prysm
│   └── 0x0a16c9a66800bd65d997db19669439281764d541ca89c15a4a10fc1782d94b1c.json
└── Teku
    ├── 0x09d60a130334aa3b9b669bf588396a007e9192de002ce66f55e5a28309b9d0d3.json
    ├── 0x421a91ebdb650671e552ce3491928d8f78e04c7c9cb75e885df90e1593ca54d6.json
    └── 0x7fedb0da9699c93ce66966555c6719e1159ae7b3220c7053a08c8f50e2f3f56f.json

You can then use this directory as the first argument to ./knn_classifier.py.

Classifier API

With pre-processed training data installed in ./training_data_proc, you can host a classification API server like this:

gunicorn --reload api_server --timeout 1800

It will take a few minutes to start-up while it loads all of the training data into memory.

Initialising classifier, this could take a moment...
Start-up complete, classifier score is 0.9886800869904645

Once it has started up, you can make POST requests to the /classify endpoint containing a single JSON-encoded block reward. There is an example input file in examples.

curl -s -X POST -H "Content-Type: application/json" --data @examples/single_teku_block.json "http://localhost:8000/classify"

The response is of the following form:

{
  "block_root": "0x421a91ebdb650671e552ce3491928d8f78e04c7c9cb75e885df90e1593ca54d6",
  "best_guess_single": "Teku",
  "best_guess_multi": "Teku",
  "probability_map": {
    "Lighthouse": 0.0,
    "Nimbus": 0.0,
    "Prysm": 0.0,
    "Teku": 1.0
  }
}

• best_guess_single is the single client that the classifier deemed most likely to have proposed this block.
• best_guess_multi is a list of 1-2 client guesses. If the classifier is more than 95% sure of a single client then the multi guess will be the same as best_guess_single. Otherwise it will be a string of the form "Lighthouse or Teku" with 2 clients in lexicographic order. 3 client splits are never returned.
• probability_map is a map from each known client label to the probability that the given block was proposed by that client.

TODO

• Improve the classification algorithm using better stats or machine learning (done, k-NN).
• Decide on data representations and APIs for presenting data to a frontend (done).
• Implement a web backend for the above API (done).
• Polish and improve all of the above.