标题： CASPER：一种具有更多负样本的智能庞氏骗局检测对比方法 显示英文标题

标题： CASPER: Contrastive Approach for Smart Ponzi Scheme Detecter with More Negative Samples

摘要： 数字货币交易的快速发展，得益于区块链技术的整合，既带来了创新，也出现了智能庞氏骗局。 智能庞氏骗局是一种在智能合约中的欺诈性投资操作，它利用新投资者的资金向早期投资者支付回报。 基于深度学习的传统庞氏骗局检测方法通常依赖于完全监督的模型，这需要大量的标记数据。 然而，这样的数据往往稀缺，阻碍了有效模型的训练。 为了解决这一挑战，我们提出了一种新的对比学习框架CASPER（具有更多负样本的智能庞氏检测对比方法），旨在增强区块链交易中的智能庞氏骗局检测。 通过利用对比学习技术，CASPER可以使用未标记的数据集学习智能合约源代码更有效的表示，显著降低运营成本和系统复杂性。 我们在XBlock数据集上评估了CASPER，在使用100%标记数据训练时，其F1得分比基线高出2.3%。 更为显著的是，在仅有25%标记数据的情况下，CASPER在相同的实验条件下，F1得分比基线高近20%。 这些结果突显了CASPER在有效且成本效益高的智能庞氏骗局检测方面的潜力，为未来可扩展的欺诈检测解决方案铺平了道路。 显示英文摘要

摘要： The rapid evolution of digital currency trading, fueled by the integration of blockchain technology, has led to both innovation and the emergence of smart Ponzi schemes. A smart Ponzi scheme is a fraudulent investment operation in smart contract that uses funds from new investors to pay returns to earlier investors. Traditional Ponzi scheme detection methods based on deep learning typically rely on fully supervised models, which require large amounts of labeled data. However, such data is often scarce, hindering effective model training. To address this challenge, we propose a novel contrastive learning framework, CASPER (Contrastive Approach for Smart Ponzi detectER with more negative samples), designed to enhance smart Ponzi scheme detection in blockchain transactions. By leveraging contrastive learning techniques, CASPER can learn more effective representations of smart contract source code using unlabeled datasets, significantly reducing both operational costs and system complexity. We evaluate CASPER on the XBlock dataset, where it outperforms the baseline by 2.3% in F1 score when trained with 100% labeled data. More impressively, with only 25% labeled data, CASPER achieves an F1 score nearly 20% higher than the baseline under identical experimental conditions. These results highlight CASPER's potential for effective and cost-efficient detection of smart Ponzi schemes, paving the way for scalable fraud detection solutions in the future.