Ponzi Scheme Detection Based on Control Flow Graph Feature Extraction-Reference-Cited by-同舟云学术

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Ponzi Scheme Detection Based on Control Flow Graph Feature Extraction

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Web Services (ICWS)
language:
Short-container-title:

Author:

Ji Shunhui¹,Huang Congxiong¹,Zhang Pengcheng¹,Dong Hai²,Xiao Yan³

Affiliation:

1. Hohai University,Key Laboratory of Water Big Data Technology of Ministry of Water Resources,Nanjing,China

2. RMIT University,School of Computing Technologies,Melbourne,Australia

3. Sun Yat-sen University,School of Cyber Science and Technology,Shenzhen,China

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10248204/10248227/10248315.pdf?arnumber=10248315

Reference35 articles.

1. A Supervised Learning Model for Detecting Ponzi Contracts in Ethereum Blockchain

2. Catboost: unbiased boosting with categorical features;prokhorenkova;Advances in neural information processing systems,2018

3. Expose Your Mask: Smart Ponzi Schemes Detection on Blockchain

4. Lightgbm: A highly efficient gradient boosting decision tree;ke;Advances in neural information processing systems,2017

5. Detecting Ethereum Ponzi Schemes Based on Improved LightGBM Algorithm

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Securing Smart Contracts in Fog Computing: Machine Learning-Based Attack Detection for Registration and Resource Access Granting;IEEE Access;2024

2. Scammer identification using CatBoost in smart contract for enhancing security in blockchain network;Wireless Networks;2023-11-12

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