Gradient boosting models flag TON DEX rug pulls with AUC up to 0.891 using five-minute trading features, but the idle-based definition's high recall is inflated because its one-hour label window overlaps the feature window.
Dual-channel Early Warning Framework for Ethereum Ponzi Schemes
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abstract
Blockchain technology supports the generation and record of transactions, and maintains the fairness and openness of the cryptocurrency system. However, many fraudsters utilize smart contracts to create fraudulent Ponzi schemes for profiting on Ethereum, which seriously affects financial security. Most existing Ponzi scheme detection techniques suffer from two major restricted problems: the lack of motivation for temporal early warning and failure to fuse multi-source information finally cause the lagging and unsatisfactory performance of Ethereum Ponzi scheme detection. In this paper, we propose a dual-channel early warning framework for Ethereum Ponzi schemes, named Ponzi-Warning, which performs feature extraction and fusion on both code and transaction levels. Moreover, we represent a temporal evolution augmentation strategy for generating transaction graph sequences, which can effectively increase the data scale and introduce temporal information. Comprehensive experiments on our Ponzi scheme datasets demonstrate the effectiveness and timeliness of our framework for detecting the Ponzi contract accounts.
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Detecting Rug Pulls in Decentralized Exchanges: Machine Learning Evidence from the TON Blockchain
Gradient boosting models flag TON DEX rug pulls with AUC up to 0.891 using five-minute trading features, but the idle-based definition's high recall is inflated because its one-hour label window overlaps the feature window.