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BitCraft v0.1.2

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@RohanOnKeys RohanOnKeys released this 02 Oct 14:10
· 39 commits to main since this release
v0.1.2
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Learn about vigilant mode.

First public release of BitCraft: AI-powered monitoring and analysis of Bitcoin transaction traffic, in your terminal. Built for Smart India Hackathon 2026, problem statement SIH26146.

Install

pipx install bitcraft      # or: pip install bitcraft
bitcraft demo

Python 3.10+, on Linux, Windows and macOS. Package: https://pypi.org/project/bitcraft/

Highlights

  • Ingestion of transaction and network metadata from CSV, JSON, JSON Lines and XML, with per-row validation and reasons for every rejected row.
  • Offline GeoIP enrichment (country, ASN, organisation) from DB-IP Lite, with Tor-exit and hosting networks flagged.
  • Entity graph linking IP addresses, wallet addresses and transactions; wallet clustering by common-input ownership; Louvain communities over the 203,769-node transaction graph.
  • Machine learning: a supervised risk model, a metadata model and an Isolation Forest, fused into one composite score.
  • Explainable alerts for transactions and wallets: confidence score, weighted drivers, SHAP reasons and evidence tagged real or modeled.
  • Terminal dashboard: dashboard, threats, graph explorer, wallets and alert detail, opened with a single bitcraft command.
  • Fully offline at runtime, with a Docker Compose stack verified on Linux and an air-gapped install bundle.

Results

On held-out timesteps 35 to 49 (16,670 labeled transactions, 6.5% illicit), the shipped ranking reaches AUC 0.899, with 100% precision in the top 100 alerts and 99.4% in the top 500. On the metadata layer, 98% of the top 100 wallet alerts are illicit owners. That layer uses planted typologies, so it measures recovery of those patterns, not real-world accuracy.

What is in this release

  • The bitcraft package on PyPI: the terminal interface. It connects to a BitCraft API, or uses built-in demo data when none is running.
  • The full system in this repository: ML pipeline (ml/), FastAPI backend (backend/), Docker Compose, and the air-gapped bundle builder (packages/offline_bundle.py).
  • Documentation: user manual, technical writeup, model card, dataset provenance and a submission checklist in docs/.

Notes

  • The datasets are not included in the repository; see docs/dataset_provenance.md.
  • A Chocolatey package is prepared in packages/chocolatey but not yet published.
  • PyPI versions 0.1.0 and 0.1.1 have the same code; 0.1.1 added the project README as the PyPI description and 0.1.2 corrects the author credits.

Authors

Rohan Pattanayak, Jagadish Prasad Pattanaik, Shreya Mishra, Shreya Mohanty, Ashutosh Badapada and Rosalin Nayak. Licensed under Apache 2.0. IP geolocation by DB-IP, CC BY 4.0.

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