Decentralized Finance (DeFi) utilizes the key principles of blockchain to improve the traditional finance system with greater freedom in trade. However, due to the absence of access restrictions in the implementation of decentralized finance protocols, effective regulatory measures are crucial to ensuring the healthy development of DeFi ecosystems. As a prominent DeFi platform, Ethereum has witnessed an increase in fraudulent activities, with the Ponzi schemes causing significant user losses. With the growing sophistication of Ponzi scheme fraud methods, existing detection techniques fail to effectively identify Ponzi schemes timely. To mitigate the risk of investor deception, we propose PSPL, a compressed sensing oversampling-based Peephole LSTM approach for detecting Ethereum Ponzi schemes. First, we identify Ethereum representative Ponzi schemes’ features by analyzing smart contracts’ codes and user accounts’ temporal transaction information based on the popular XBlock dataset. Second, to address the class imbalance and few-shot learning challenges, we leverage the compressed sensing approach to oversample the Ponzi Scheme samples. Third, peephole LSTM is employed to effectively capture long sequence variations in the fraud features of Ponzi schemes, accurately identifying hidden Ponzi schemes during the transaction process in case fraudulent features are exposed. Finally, experimental results demonstrate the effectiveness and efficiency of PSPL.

中文翻译：

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PSPL：一种基于压缩感知过采样的窥视孔 LSTM 的庞氏骗局智能合约检测方法


去中心化金融 （DeFi） 利用区块链的关键原理来改进传统金融系统，提高贸易自由度。然而，由于去中心化金融协议的实施没有访问限制，有效的监管措施对于确保 DeFi 生态系统的健康发展至关重要。作为著名的 DeFi 平台，以太坊的欺诈活动有所增加，庞氏骗局造成了巨大的用户损失。随着庞氏骗局欺诈方法的日益复杂，现有的检测技术无法及时有效地识别庞氏骗局。为了降低投资者欺骗的风险，我们提出了 PSPL，这是一种基于压缩传感过采样的 Peephole LSTM 方法，用于检测以太坊庞氏骗局。首先，我们通过基于流行的 XBlock 数据集分析智能合约的代码和用户账户的临时交易信息来识别以太坊代表性庞氏骗局的特征。其次，为了解决班级不平衡和小样本学习挑战，我们利用压缩传感方法对庞氏骗局样本进行过采样。第三，窥视孔 LSTM 用于有效捕获庞氏骗局欺诈特征的长序列变化，在交易过程中准确识别隐藏的庞氏骗局，以防欺诈特征暴露。最后，实验结果证明了 PSPL 的有效性和效率。