Improving Robustness and Accuracy of Ponzi Scheme Detection on Ethereum Using Time-Dependent Features

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

Bayesian Optimization with Missing Inputs

Bayesian optimization (BO) is an efficient method for optimizing expensive black-box functions. In real-world applications, BO often faces a major problem of missing values in inputs. The missing inputs can happen in two cases. First, the historical data for training BO often contain missing values. Second, when performing the function evaluation (e.g. computing alloy strength in a heat treatment process), errors may occur (e.g. a thermostat stops working) leading to an erroneous situation where the function is computed at a random unknown value instead of the suggested value. To deal with this problem, a common approach just simply skips data points where missing values happen. Clearly, this naive method cannot utilize data efficiently and often leads to poor performance. In this paper, we propose a novel BO method to handle missing inputs. We first find a probability distribution of each missing value so that we can impute the missing value by drawing a sample from its distribution. We then develop a new acquisition function based on the well-known Upper Confidence Bound (UCB) acquisition function, which considers the uncertainty of imputed values when suggesting the next point for function evaluation. We conduct comprehensive experiments on both synthetic and real-world applications to show the usefulness of our method.