The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. However, previous studies have primarily concentrated on de-anonymization and phishing nodes, neglecting the distinctive features of web3 scams. Moreover, the current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features. However, large-scale transaction networks with temporal attributes conform to a power-law distribution, posing challenges in detecting web3 scams. To overcome these challenges, we present ScamSweeper, anovelframework that emphasizes the dynamic evolution of transaction graphs, to identify web3 scams on Ethereum. ScamSweeper samples the network with a structure temporal random walk, which is an optimized sample walking method that considers both temporal attributes and structural information. Then, the directed graph encoder generates the features of each subgraph during different temporal intervals, sorting as a sequence. Moreover, a variational Transformer is utilized to extract the dynamic evolution in the subgraph sequence. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts, which are from the first 18 million block heights on Ethereum. Subsequently, we comprehensively analyze the distinctions in various attributes, including nodes, edges, and degree distribution. Our experiments indicate that ScamSweeper outperforms SIEGE, Ethident, and PDTGA in detecting web3 scams, achieving a weighted F1-score improvement of at least 17.29% with the base value of 0.59. In addition, ScamSweeper in phishing node detection achieves at least a 17.5% improvement over DGTSG and BERT4ETH in F1-score from 0.80.

As the Ethereum platform continues to mature and gain widespread usage, it is crucial to maintain high standards of smart contract writing practices. While bad practices in smart contracts may not directly lead to security issues, they do elevate the risk of encountering problems. Therefore, to understand and avoid these bad practices, this paper introduces the first systematic study of bad practices in smart contracts, delving into over 35 specific issues. Specifically, we propose a large language models (LLMs)-based framework, SCALM. It combines Step-Back Prompting and Retrieval-Augmented Generation (RAG) to effectively identify and address various bad practices. Our extensive experiments using multiple LLMs and datasets have shown that SCALM outperforms existing tools in detecting bad practices in smart contracts.

The decentralized exchange (DEX) leverages smart contracts to trade digital assets for users on the blockchain. Developers usually develop several smart contracts into one project, implementing complex logic functions and multiple transaction operations. However, the interaction among these contracts poses challenges for developers analyzing the state logic. Due to the complex state logic in DEX projects, many critical state derailment defects have emerged in recent years. In this article, we conduct the first systematic study of state derailment defects in DEX. We define five categories of state derailment defects and provide detailed analyses of them. Furthermore, we propose a novel deep learning-based framework StateGuardfor detecting state derailment defects in DEX smart contracts. It leverages a smart contract deconstructor to deconstruct the contract into an abstract syntax tree (AST), from which five categories of dependency features are extracted. Next, it implements a graph optimizer to process the structured data. At last, the optimized data is analyzed by graph convolutional networks to identify potential state derailment defects. We evaluated StateGuardthrough a dataset of 46 DEX projects containing 5671 smart contracts, and it achieved 94.25% F1-score. In addition, in a comparison experiment with state-of-the-art, StateGuardleads the F1-score by 6.29%. To further verify its practicality, we used StateGuardto audit real-world contracts and successfully authenticated multiple novel common vulnerabilities and exposures.

The detection of vulnerabilities in smart contracts remains a significant challenge. While numerous tools are available for analyzing smart contracts in source code, only about 1.79% of smart contracts on Ethereum are open-source. For existing tools that target bytecodes, most of them only consider the semantic logic context and disregard function interface information in the bytecodes. In this paper, we propose COBRA, a novel framework that integrates semantic context and function interfaces to detect vulnerabilities in bytecodes of the smart contract. To our best knowledge, COBRA is the first framework that combines these two features. Moreover, to infer the function signatures that are not present in signature databases, we present SRIF (Signatures Reverse Inference from Functions), automatically learn the rules of function signatures from the smart contract bytecodes. The bytecodes associated with the function signatures are collected by constructing a control flow graph (CFG) for the SRIF training. We optimize the semantic context using the operation code in the static single assignment (SSA) format. Finally, we integrate the context and function interface representations in the latent space as the contract feature embedding. The contract features in the hidden space are decoded for vulnerability classifications with a decoder and attention module. Experimental results demonstrate that SRIF can achieve 94.76% F1-score for function signature inference. Furthermore, when the ground truth ABI exists, COBRA achieves 93.45% F1-score for vulnerability classification. In the absence of ABI, the inferred function feature fills the encoder, and the system accomplishes an 89.46% recall rate.