Al-SPSD: Anti-leakage smart Ponzi schemes detection in blockchain. Issue 4 (July 2021)
- Record Type:
- Journal Article
- Title:
- Al-SPSD: Anti-leakage smart Ponzi schemes detection in blockchain. Issue 4 (July 2021)
- Main Title:
- Al-SPSD: Anti-leakage smart Ponzi schemes detection in blockchain
- Authors:
- Fan, Shuhui
Fu, Shaojing
Xu, Haoran
Cheng, Xiaochun - Abstract:
- Abstract: Blockchain provides a decentralized environment for applications and information systems in various fields. It is an innovative revolution for the traditional Internet. However, without proper regulatory mechanisms, the blockchain technology has gradually become a hotbed of criminal activities, such as Ponzi scheme that brings huge economic losses to people. To maintain the security of the blockchain system, the machine learning technique, which can detect smart Ponzi schemes automatically has recently received extensive attention. However, the existing method has potential target leakage and prediction shift problems when dealing with category features and calculating gradient estimates. Besides, they also ignore the imbalance and repeatability of smart contracts, which often causes the model to overfit. In this paper, we introduce a novel method for detecting smart Ponzi schemes in blockchain. Specifically, we first expand the dataset of smart Ponzi schemes and eliminate the unbalanced dataset via data enhancement. Then, we leverage ordered target statistics (TS) to handle the category features of smart contract without target leakage. Finally, we propose an anti-leakage smart Ponzi schemes detection (Al-SPSD) model based on the idea of ordered boosting. Experimental results show that our proposal outperforms the competitive methods and is effective and reliable in detecting smart Ponzi schemes. Al-SPSD achieves 96% F-score and detects about 1, 621 active smartAbstract: Blockchain provides a decentralized environment for applications and information systems in various fields. It is an innovative revolution for the traditional Internet. However, without proper regulatory mechanisms, the blockchain technology has gradually become a hotbed of criminal activities, such as Ponzi scheme that brings huge economic losses to people. To maintain the security of the blockchain system, the machine learning technique, which can detect smart Ponzi schemes automatically has recently received extensive attention. However, the existing method has potential target leakage and prediction shift problems when dealing with category features and calculating gradient estimates. Besides, they also ignore the imbalance and repeatability of smart contracts, which often causes the model to overfit. In this paper, we introduce a novel method for detecting smart Ponzi schemes in blockchain. Specifically, we first expand the dataset of smart Ponzi schemes and eliminate the unbalanced dataset via data enhancement. Then, we leverage ordered target statistics (TS) to handle the category features of smart contract without target leakage. Finally, we propose an anti-leakage smart Ponzi schemes detection (Al-SPSD) model based on the idea of ordered boosting. Experimental results show that our proposal outperforms the competitive methods and is effective and reliable in detecting smart Ponzi schemes. Al-SPSD achieves 96% F-score and detects about 1, 621 active smart Ponzi schemes in Ethereum. Highlights: We propose a novel anti-leakage smart Ponzi schemes detection method named Al-SPSD. We expand the dataset and eliminate the unbalanced dataset via data enhancement. We find that our proposal outperforms the competitive methods in terms of performance. … (more)
- Is Part Of:
- Information processing & management. Volume 58:Issue 4(2021)
- Journal:
- Information processing & management
- Issue:
- Volume 58:Issue 4(2021)
- Issue Display:
- Volume 58, Issue 4 (2021)
- Year:
- 2021
- Volume:
- 58
- Issue:
- 4
- Issue Sort Value:
- 2021-0058-0004-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-07
- Subjects:
- Blockchain -- Smart Ponzi scheme -- Ethereum -- Machine learning -- Data mining
Information storage and retrieval systems -- Periodicals
Information science -- Periodicals
Systèmes d'information -- Périodiques
Sciences de l'information -- Périodiques
Information science
Information storage and retrieval systems
Periodicals
658.4038 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064573 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ipm.2021.102587 ↗
- Languages:
- English
- ISSNs:
- 0306-4573
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4493.893000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 16813.xml