| 摘要: |
| 以太坊上智能合约的广泛部署为区块链生态系统注入了活力, 而智能合约的不可逆性和匿名性却给监管带来了巨大挑战. 不法分子趁机在以太坊部署庞氏骗局, 引发了严重的安全风险和经济损失. 因此, 迅速高效地检测庞氏骗局智能合约至关重要. 目前的庞氏骗局检测方法存在的主要挑战包括智能合约操作码行为特征被忽略, 特征提取不全面, 检测方法在受到对抗干扰时性能不稳定、准确率低等问题. 为克服这些不足, 提出一种融合TextCNN和对抗训练的以太坊庞氏骗局检测方法. 该方法通过静态分析智能合约操作码来提取智能合约的行为特征, 同时结合Word2Vec模型保留智能合约的语义信息, 确保了操作码特征的完整性. 与此同时, 还采用改进后的动态步长投影梯度下降算法训练TextCNN模型, 增强检测模型的鲁棒性, 提高检测准确率. 在XBlock数据集上展开实验, 实验结果表明, 所提方法在确保精确率和鲁棒性的同时, 召回率达到98.36%, F1分数达到98.31%. 该方法重点关注智能合约操作码而不依赖交易特征, 能在智能合约部署时迅速、高效地检测出庞氏骗局智能合约. |
| 关键词: 智能合约 庞氏骗局 行为序列 对抗训练 TextCNN |
| DOI：10.13328/j.cnki.jos.007564 |
| 分类号:TP393 |
| 基金项目:国家自然科学基金(92267206, 62032013, 62432003); 广东省重点领域研发计划(2020B0101090005) |
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| Ethereum Ponzi Scheme Detection Model Combining TextCNN and Adversarial Training |
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NING Xiao-Yong1, GAO Rui-Jie2, YE Chu-Han2, LIU Yuan3, WANG Xing-Wei1, HUANG Min4
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1.School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China;2.Software College, Northeastern University, Shenyang 110819, China;3.Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China;4.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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| Abstract: |
| The widespread deployment of smart contracts on Ethereum has injected vitality into the blockchain ecosystem, while the irreversibility and anonymity of smart contracts have posed great challenges to supervision. Criminals take the opportunity to deploy Ponzi schemes on Ethereum, causing serious security risks and economic losses. Therefore, it is essential to detect Ponzi scheme smart contracts quickly and efficiently. The main challenges of the current Ponzi scheme detection method include the neglect of the behavior characteristics of smart contract opcodes, incomplete feature extraction, unstable performance, and low accuracy of the detection method when the method is subjected to anti-interference. To overcome these shortcomings, this study proposes an Ethereum Ponzi scheme detection method that combines TextCNN and adversarial training. This method extracts the behavioral characteristics of smart contracts by the static analysis of smart contracts, and combines the Word2Vec model to retain the semantic information of smart contracts to ensure the integrity of the opcode features. Meanwhile, the improved dynamic step projection gradient descent algorithm is adopted to train the TextCNN model to enhance the robustness of the detection model and improve the detection accuracy. Experiments carried out on the XBlock dataset show that the proposed method achieves a Recall of 98.36% and F1-score of 98.31% while ensuring precision and robustness. The method focuses on smart contract opcodes without relying on transaction features, and can quickly and efficiently detect Ponzi scheme smart contracts at the time of smart contract deployment. |
| Key words: smart contract Ponzi scheme behavioral sequence adversarial training TextCNN |
