CASPER: Contrastive Approach for Smart Ponzi Scheme Detecter with More Negative Samples
This addresses fraud detection in blockchain for investors and platforms, but it is incremental as it applies an existing contrastive learning technique to a specific domain problem.
The paper tackles the problem of detecting smart Ponzi schemes in blockchain transactions, where labeled data is scarce, by proposing a contrastive learning framework called CASPER. It achieves a 2.3% higher F1 score with full labeled data and nearly 20% higher with only 25% labeled data compared to baselines.
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.