Jie Shen

Jiajun Zhou, Chenkai Hu, Shenbo Gong et al.

Blockchain technology has the characteristics of decentralization, traceability and tamper proof, which creates a reliable decentralized transaction mode, further accelerating the development of the blockchain platforms. However, with the popularization of various financial applications, security problems caused by blockchain digital assets, such as money laundering, illegal fundraising and phishing fraud, are constantly on the rise. Therefore, financial security has become an important issue in the blockchain ecosystem, and identifying the types of accounts in blockchain (e.g. miners, phishing accounts, Ponzi contracts, etc.) is of great significance in risk assessment and market supervision. In this paper, we construct an account interaction graph using raw blockchain data in a graph perspective, and proposes a joint learning framework for account identity inference on blockchain with graph contrast. We first capture transaction feature and correlation feature from interaction graph, and then perform sampling and data augmentation to generate multiple views for account subgraphs, finally jointly train the subgraph contrast and account classification task. Extensive experiments on Ethereum datasets show that our method achieves significant advantages in account identity inference task in terms of classification performance, scalability and generalization.

Shanqing Yu, Jie Jin, Yunyi Xie et al.

With the rapid growth of blockchain, an increasing number of users have been attracted and many implementations have been refreshed in different fields. Especially in the cryptocurrency investment field, blockchain technology has shown vigorous vitality. However, along with the rise of online business, numerous fraudulent activities, e.g., money laundering, bribery, phishing, and others, emerge as the main threat to trading security. Due to the openness of Ethereum, researchers can easily access Ethereum transaction records and smart contracts, which brings unprecedented opportunities for Ethereum scams detection and analysis. This paper mainly focuses on the Ponzi scheme, a typical fraud, which has caused large property damage to the users in Ethereum. By verifying Ponzi contracts to maintain Ethereum's sustainable development, we model Ponzi scheme identification and detection as a node classification task. In this paper, we first collect target contracts' transactions to establish transaction networks and propose a detecting model based on graph convolutional network (GCN) to precisely distinguishPonzi contracts. Experiments on different real-world Ethereum datasets demonstrate that our proposed model has promising results compared with general machine learning methods to detect Ponzi schemes.

Jie Shen, Jiajun Zhou, Yunyi Xie et al.

The anonymity of blockchain has accelerated the growth of illegal activities and criminal behaviors on cryptocurrency platforms. Although decentralization is one of the typical characteristics of blockchain, we urgently call for effective regulation to detect these illegal behaviors to ensure the safety and stability of user transactions. Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. As a common tool, graph mining technique can effectively represent the interactive information between accounts and be used for identity inference. However, existing methods cannot balance scalability and end-to-end architecture, resulting high computational consumption and weak feature representation. In this paper, we present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in large-scale graph. Furthermore, we propose a generic end-to-end graph neural network model, named $\text{I}^2 \text{BGNN}$, which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity, achieving de-anonymization. Extensive experiments on EOSG and ETHG datasets demonstrate that the proposed method achieve the state-of-the-art performance in identity inference.