Scam Token Detection Based on Static Analysis Before Contract Deployment

Scam Token Detection Based on Static Analysis Before Contract Deployment

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연구 분야: Verification

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학회: International Conference on Financial Cryptography and Data Security

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초록

In recent years, the number of crimes using smart contracts has increased. In particular, fraud using tokens, such as rug-pull, has become an ignorable issue in the field of decentralized finance because a lot of users have been scammed. Therefore, constructing a detection system for scam tokens is an urgent need. Existing methods are based on machine learning, and they use transaction and liquidity data as features. However, they cannot completely remove the risk of being scammed because these features can be extracted after scam tokens are deployed to blockchain. In this paper, we propose a scam token detection system based on static analysis. In order to detect scam tokens before deployment, we utilize code-based data, such as bytecodes and opcodes, because they can be obtained before contract deployment. Since N-gram includes information regarding the order of code sequences and scam tokens have the specific order of code-based data, we adopt N-gram of them as features. Furthermore, for the purpose of achieving a high detection performance, each feature is categorized into a scam-oriented feature or benign-oriented one to make differences in the values of feature vectors between scam and benign token. Our results show the effectiveness of code-based data for detection by achieving a higher F1-score compared to the methods in another field of fraud detection in Ethereum based on code-based data. In addition, we also confirmed that the position of effective code for detection is near the start position of runtime code in our experiments.

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