Improving Robustness and Accuracy of Ponzi Scheme Detection on Ethereum
  Using Time-Dependent Features
Phuong Duy Huynh Son Hoang Dau Xiaodong Li Phuc Luong Emanuele Viterbo
The rapid development of blockchain has led to more and more funding pouring
into the cryptocurrency market, which also attracted cybercriminals' interest
in recent years. The Ponzi scheme, an old-fashioned fraud, is now popular on
the blockchain, causing considerable financial losses to many crypto-investors.
A few Ponzi detection methods have been proposed in the literature, most of
which detect a Ponzi scheme based on its smart contract source code or opcode.
The contract-code-based approach, while achieving very high accuracy, is not
robust: first, the source codes of a majority of contracts on Ethereum are not
available, and second, a Ponzi developer can fool a contract-code-based
detection model by obfuscating the opcode or inventing a new profit
distribution logic that cannot be detected (since these models were trained on
existing Ponzi logics only). A transaction-based approach could improve the
robustness of detection because transactions, unlike smart contracts, are
harder to be manipulated. However, the current transaction-based detection
models achieve fairly low accuracy. We address this gap in the literature by
developing new detection models that rely only on the transactions, hence
guaranteeing the robustness, and moreover, achieve considerably higher
Accuracy, Precision, Recall, and F1-score than existing transaction-based
models. This is made possible thanks to the introduction of novel
time-dependent features that capture Ponzi behaviours characteristics derived
from our comprehensive data analyses on Ponzi and non-Ponzi data from the
XBlock-ETH repository