Code Monkey home page Code Monkey logo

sadponzi's Introduction

SADPonzi

SADPonzi is a detector for Ponzi scheme smart contracts (ponzitract). It is based on symbolic execution technology. We implement it atop teEther.

Benchmark

  • Ground Truth Benchmark. We filter the dataset used in https://github.com/blockchain-unica/ethereum-ponzi and get 133 ground truth ponzitracts (see our paper for details). At the meantimes, we collect 1,262 DApp as non-Ponzi cases from dapptotal.com. (see link)
  • Robustness Benchark. We generate four groups of data for the robustness experiment. (see link)
  • Large-scale Benchmark. We apply SADPonzi to all the 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme smart contracts in total, with a volume of over 17 million US Dollar invested from victims. (see link)

Setup

SADPonzi was tested on an Ubuntu 18.04 server with Python 3.8.10. Check the environment requirements of teEther.

$ git clone https://github.com/Kenun99/tse_sadponzi.git sadponzi && cd sadponzi
$ python3 -m venv ./venv && source ./venv/bin/activate # activate your virtual environment
$ python -m pip install -r ./requirements.txt		   # install packages

Run the examples

  1. Goto the project folder sadponzi/.
  2. Setup a dictionary to save the results. e.g., mkdir ./eval_results
  3. Run for RQ1. python ./effectiveness.py <path_to_RQ1_benchmark> <path_to_results>. For example, python ./effectiveness.py /RQ1/ponzi ./eval_results. Obtain the RQ1 benchmark from here.
  4. Verify the results. If you find False Negatives, please set a larger timeout and run SADPonzi again.

Academia

Our paper SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts was published at the ACM Sigmetrics' 21.

@article{10.1145/3460093,
author = {Chen, Weimin and Li, Xinran and Sui, Yuting and He, Ningyu and Wang, Haoyu and Wu, Lei and Luo, Xiapu},
title = {SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts},
year = {2021},
issue_date = {June 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {5},
number = {2},
url = {https://doi.org/10.1145/3460093},
doi = {10.1145/3460093},
journal = {Proc. ACM Meas. Anal. Comput. Syst.},
month = jun,
articleno = {26},
numpages = {30},
keywords = {symbolic execution, smart contract, ethereum, Ponzi scheme}
}

sadponzi's People

Contributors

kenun99 avatar ran-2012 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.