计算机科学
利用
深度学习
人工智能
数据库事务
人工神经网络
机器学习
计算机安全
数据库
作者
Bin Wang,Xiaohan Yuan,Li Duan,Bin Wang,Bin Wang,Chunhua Su,Bin Wang
出处
期刊:IEEE Transactions on Computational Social Systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-12
被引量:6
标识
DOI:10.1109/tcss.2022.3228122
摘要
With the rapid development of decentralized financial (DeFi), the total value locked (TVL) in DeFi continues to increase. A big number of adversaries exploit logic vulnerabilities to attack DeFi applications for profit, such as flash loan attacks and price manipulation attacks. However, the current vulnerability detection tools for smart contracts cannot be directly used to detect the logic vulnerabilities generated by the combination of different protocols. How to characterize and detect DeFi attacks that exploited logic vulnerabilities is a big challenge. In this work, we propose a deep-learning-based attack detection system on DeFi, called DeFiScanner, in which we design a novel neural network that includes a global model, a local model, and a fusion model to characterize DeFi attacks. First, the unstructured emitted events are automatically and efficiently normalized. Second, the transaction-related features of normalized emitted events are enriched with the global model and the semantic features of emitted events are extracted with the local model. Finally, the transaction-related features and the semantic features of emitted events are fused efficiently with the fusion model to detect DeFi attacks. We collect a dataset that consists of 50 910 real-world DeFi transactions on Ethereum (ETH). The extensive experimental results demonstrate the effectiveness of DeFiScanner. The true positive rate (TPR) and the area under the receiver operating characteristic (ROC) curve of the system reach 0.91 and 0.97, respectively.
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