Recently, blockchain technology has become a topic in the spotlight but also a hotbed of various cybercrimes. Among them, phishing scams on blockchain have been found to make a notable amount of money, thus emerging as a serious threat to the trading security of the blockchain ecosystem. In order to create a favorable environment for investment, an effective method for detecting phishing scams is urgently needed in the blockchain ecosystem. To this end, this article proposes an approach to detect phishing scams on Ethereum by mining its transaction records. Specifically, we first crawl the labeled phishing addresses from two authorized websites and reconstruct the transaction network according to the collected transaction records. Then, by taking the transaction amount and timestamp into consideration, we propose a novel network embedding algorithm called trans2vec to extract the features of the addresses for subsequent phishing identification. Finally, we adopt the one-class support vector machine (SVM) to classify the nodes into normal and phishing ones. Experimental results demonstrate that the phishing detection method works effectively on Ethereum, and indicate the efficacy of trans2vec over existing state-of-the-art algorithms on feature extraction for transaction networks. This work is the first investigation on phishing detection on Ethereum via network embedding and provides insights into how features of large-scale transaction networks can be embedded.

As the largest public blockchain-based platform supporting smart contracts, Ethereum has accumulated a large number of user transaction records since its debut in 2014. Analysis of Ethereum transaction records, however, is still relatively unexplored till now. Modeling the transaction records as a static simple graph, existing methods are unable to accurately characterize the temporal and multiplex features of the edges. In this brief, we first model the Ethereum transaction records as a complex network by incorporating time and amount features of the transactions, and then design several flexible temporal walk strategies for random-walk based graph representation of this large-scale network. Experiments of temporal link prediction on real Ethereum data demonstrate that temporal information and multiplicity characteristic of edges are indispensable for accurate modeling and understanding of Ethereum transaction networks.

At present, the concept of metaverse has sparked widespread attention from the public to major industries. With the rapid development of blockchain and Web3 technologies, the decentralized metaverse ecology has attracted a large influx of users and capital. Due to the lack of industry standards and regulatory rules, the Web3-empowered metaverse ecosystem has witnessed a variety of financial crimes, such as scams, code exploit, wash trading, money laundering, and illegal services and shops. To this end, it is especially urgent and critical to summarize and classify the financial security threats on the Web3-empowered metaverse in order to maintain the long-term healthy development of its ecology. In this paper, we first outline the background, foundation, and applications of the Web3 metaverse. Then, we provide a comprehensive overview and taxonomy of the security risks and financial crimes that have emerged since the development of the decentralized metaverse. For each financial crime, we focus on three issues: a) existing definitions, b) relevant cases and analysis, and c) existing academic research on this type of crime. Next, from the perspective of academic research and government policy, we summarize the current anti-crime measurements and technologies in the metaverse. Finally, we discuss the opportunities and challenges in behavioral mining and the potential regulation of financial activities in the metaverse. The overview of this paper is expected to help readers better understand the potential security threats in this emerging ecology, and to provide insights and references for financial crime fighting.

Recently, graph embedding techniques have been widely used in the analysis of various networks, but most of the existing embedding methods omit the network dynamics and the multiplicity of edges, so it is difficult to accurately describe the detailed characteristics of the transaction networks. Ethereum is a blockchain-based platform supporting smart contracts. The open nature of blockchain makes the transaction data on Ethereum completely public and also brings unprecedented opportunities for transaction network analysis. By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG) where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned a timestamp. We then define the problem of Temporal Weighted Multidigraph Embedding (T-EDGE) by incorporating both temporal and weighted information of the edges, the purpose being to capture more comprehensive properties of dynamic transaction networks. To evaluate the effectiveness of the proposed embedding method, we conduct experiments of node classification on real-world transaction data collected from Ethereum. Experimental results demonstrate that T-EDGE outperforms baseline embedding methods, indicating that time-dependent walks and the multiplicity characteristic of edges are informative and essential for time-sensitive transaction networks.

With the overall momentum of the blockchain industry, financial crimes related to blockchain crypto-assets are becoming increasingly prevalent. After committing a crime, the main goal of cybercriminals is to obfuscate the source of the illicit funds in order to convert them into cash and get away with it. Many studies have analyzed money laundering (ML) in the field of the traditional financial sector. However, in terms of the emerging blockchain crypto-asset ecosystem, there is currently only one public anti-money laundering (AML) dataset for Bitcoin – the Elliptic dataset, whose binary labels (licit vs. illicit transactions) cannot cover the ML behaviors in the evergrowing crypto-asset market. To fill this gap, in this paper, we propose a framework named XBlockFlow which identifies ML addresses starting from Ethereum heist incidents and obtains the first detailed Ethereum ML dataset named EthereumHeist , and then conducts a comprehensive feature and evolution analysis on the EthereumHeist dataset according to the three main phases of ML. We first search for the source cybercriminal accounts including exchange hackers, DeFi exploiters, and scammers. Then, employing the idea of taint analysis, we track the diverse downstream transactions and addresses layer by layer. At the end of tracking, we identify and categorize service providers, and go a step further to investigate advanced ML methods that do not exist in the Bitcoin scenario, e.g. token swap and counterfeit token creation. Based on the ML identification results, we obtain many interesting findings about crypto-asset money laundering, observing the escalating money laundering methods such as creating counterfeit tokens and masquerading as speculators.