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

TL;DR

CASPER introduces a contrastive learning framework that improves smart Ponzi scheme detection in blockchain transactions, especially when labeled data is scarce, by learning effective representations from unlabeled data.

Contribution

The paper presents a novel contrastive learning approach, CASPER, that enhances detection accuracy of smart Ponzi schemes using unlabeled data, reducing reliance on labeled datasets.

Findings

CASPER outperforms baseline by 2.3% in F1 score with full labeled data.

With only 25% labeled data, CASPER achieves nearly 20% higher F1 score than baseline.

CASPER effectively leverages unlabeled data for fraud detection in blockchain transactions.

Abstract

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.