Fraud Detection

In the blockchain ecosystem, there are many illegal activities, such as Ponzi schemes, hacker attacks, market manipulations and so on, which seriously threaten the development of blockchain technology.

We establish identification and early warning models for various illegal behaviors based on blockchain data to promote the safe and orderly development of blockchain technology.

Smart Ponzi Scheme Detection

Smart Ponzi schemes are Ponzi schemes implemented by smart contracts on blockchain. The goal of this sub-project is to build identification and early warning models for smart Ponzi scheme contracts based on machine learning and data mining method.

Detecting Ponzi Schemes on Ethereum

Introduction

We proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and  operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the pro-posed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.

Citation

BibTeX

@inproceedings{chen2018detecting,  
title={Detecting ponzi schemes on ethereum: Towards healthier blockchain technology},
author={Chen, Weili and Zheng, Zibin and Cui, Jiahui and Ngai, Edith and Zheng, Peilin and Zhou, Yuren},
booktitle={Proceedings of the 2018 World Wide Web Conference},
pages={1409--1418},
year={2018},
organization={International World Wide Web Conferences Steering Committee}
}

IEEE

W. Chen, Z. Zheng, J. Cui, E. Ngai, P. Zheng, and Y. Zhou, “Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology,” pp. 1409-1418, 2018, doi: 10.1145/3178876.3186046.

ACM

Weili Chen, Zibin Zheng, Jiahui Cui, Edith Ngai, Peilin Zheng, and Yuren Zhou. 2018. Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology. In Proceedings of the 2018 World Wide Web Conference (WWW ’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1409-1418. DOI:https://doi.org/10.1145/3178876.3186046

Exploiting Blockchain Data to Detect Smart Ponzi Schemes on Ethereum

Introduction

To help to deal with Ponzi scheme and to provide reusable research data sets for future research, this paper collects real-world samples and proposes an approach to detect Ponzi schemes implemented as smart contracts (i.e., smart Ponzi schemes) on the blockchain. First, 200 smart Ponzi schemes are obtained by manually checking more than 3,000 open source smart contracts on the Ethereum platform. Then, two kinds of features are extracted from the transaction history and operation codes of the smart contracts. Finally, a classification model is presented to detect smart Ponzi schemes. The extensive experiments show that the proposed model performs better than many traditional classification models and can achieve high accuracy for practical use. By using the proposed approach, we estimate that there are more than 500 smart Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.

Citation

BibTeX

@article{chen2019exploiting, 
title={Exploiting Blockchain Data to Detect Smart Ponzi Schemes on Ethereum},
author={Chen, Weili and Zheng, Zibin and Ngai, Edith C-H and Zheng, Peilin and Zhou, Yuren},
journal={IEEE Access},
volume={7},
pages={37575--37586},
year={2019},
publisher={IEEE}
}

IEEE

W. Chen, Z. Zheng, E. C. -. Ngai, P. Zheng and Y. Zhou, "Exploiting Blockchain Data to Detect Smart Ponzi Schemes on Ethereum," in IEEE Access, vol. 7, pp. 37575-37586, 2019.

ACM

Weili Chen, Zibin Zheng, Edith C.H. Ngai, Peilin Zheng, and Yuren Zhou. 2019. Exploiting Blockchain Data to Detect Smart Ponzi Schemes on Ethereum. IEEE Access 7, (2019), 37575–37586. DOI:https://doi.org/10.1109/ACCESS.2019.2905769

Market Ecosystem Analysis

The birth of Bitcoin ushered in the era of cryptocurrency, which has now become a special financial market attracted extensive attention worldwide. This sub-project aims to analyze the new financial ecosystem and provides abnormal behavior detection for participants in the ecosystem.

Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network

Introduction

The cryptocurrency market is a very huge market without effective supervision. It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. This paper proposes an approach to mine the transaction networks of exchanges for answering this question. By taking the leaked transaction history of Mt. Gox Bitcoin exchange as a sample, we first divide the accounts into three categories according to its characteristic and then construct the transaction history into three graphs.Many observations and findings are obtained via analyzing the constructed graphs. To evaluate the influence of the accounts’transaction behavior on the Bitcoin exchange price, the graphs are reconstructed into series and reshaped as matrices. By using singular value decomposition (SVD) on the matrices, we identify many base networks which have a great correlation with the price fluctuation. When further analyzing the most important accounts in the base networks, plenty of market manipulation patterns are found. According to these findings, we conclude that there was serious market manipulation in Mt. Gox exchange and the cryptocurrency market must strengthen the supervision.

Citation

BibTeX

@inproceedings{
chen2019market,
title={Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network},
author={Chen, Weili and Wu, Jun and Zheng, Zibin and Chen, Chuan and Zhou, Yuren},
booktitle={IEEE Conference on Computer Communications},
pages={964--972},
year={2019},
organization={IEEE}
}

IEEE

W. Chen, J. Wu, Z. Zheng, C. Chen and Y. Zhou, "Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network," IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, Paris, France, 2019, pp. 964-972.

ACM

Weili Chen, Jun Wu, Zibin Zheng, Chuan Chen, and Yuren Zhou. 2019. Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network. Proceedings - IEEE INFOCOM 2019-April, April 2011: 964-972. https://doi.org/10.1109/INFOCOM.2019.8737364