Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem
Abstract
In recent years, blockchain technology has created a new cryptocurrency world and has attracted a lot of attention. It also is rampant with various scams. For example, phishing scams have grabbed a lot of money and has become an important threat to users' financial security in the blockchain ecosystem. To help deal with this issue, this paper proposes a systematic approach to detect phishing accounts based on blockchain transactions and take Ethereum as an example to verify its effectiveness. Specifically, we propose a graph-based cascade feature extraction method based on transaction records and a lightGBM-based Dual-sampling Ensemble algorithm to build the identification model. Extensive experiments show that the proposed algorithm can effectively identify phishing scams.
Cite
Text
Chen et al. "Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/621Markdown
[Chen et al. "Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/chen2020ijcai-phishing/) doi:10.24963/IJCAI.2020/621BibTeX
@inproceedings{chen2020ijcai-phishing,
title = {{Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem}},
author = {Chen, Weili and Guo, Xiongfeng and Chen, Zhiguang and Zheng, Zibin and Lu, Yutong},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {4506-4512},
doi = {10.24963/IJCAI.2020/621},
url = {https://mlanthology.org/ijcai/2020/chen2020ijcai-phishing/}
}