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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 paper, 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 StateGuard for 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 (GCNs) to identify potential state derailment defects. We evaluated StateGuard through a dataset of 46 DEX projects containing 5,671 smart contracts, and it achieved 94.25% F1-score. In addition, in a comparison experiment with state-of-the-art, StateGuard leads the F1-score by 6.29%. To further verify its practicality, we used StateGuar to audit real-world contracts and successfully authenticated multiple novel CVEs., Comment: 13 pages

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. 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. In this paper, we present ScamSweeper, a novel framework to identify web3 scams on Ethereum. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts. Our experiments indicate that ScamSweeper exceeds the state-of-the-art in detecting web3 scams., Comment: This work is accepted by ASE'24

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., Comment: This work is accepted by ASE'24

Smart contracts are pivotal for implementing various functions due to their interactivity with external data. However, this interactivity also presents challenges in terms of security and reliability. There is a lack of statistical and quantitative research on the interaction between smart contracts and external data. To fill this gap, we thoroughly examine 10,500 actual smart contracts to select 9,356 valid samples, excluding those that are outdated or have compilation errors. Utilizing code parsing techniques, the study transformed contract code into Abstract Syntax Trees (ASTs) and extracted keywords related to external data dependency through code analysis. By comparing the ASTs with the keyword list, we conduct a quantitative analysis of the number and proportion of contracts involving external data interaction. Furthermore, we collect over 3,600 security audit reports and manually filter 249 (approximately 9%) reports related to external data interaction, categorizing the external data dependency in these contracts. We also explore the relationship between the complexity of smart contracts and their dependence on external data.

The openness and transparency of Ethereum transaction data make it easy to be exploited by any entities, executing malicious attacks. The sandwich attack manipulates the Automated Market Maker (AMM) mechanism, profiting from manipulating the market price through front or after-running transactions. To identify and prevent sandwich attacks, we propose a cascade classification framework GasTrace. GasTrace analyzes various transaction features to detect malicious accounts, notably through the analysis and modeling of Gas features. In the initial classification, we utilize the Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel to generate the predicted probabilities of accounts, further constructing a detailed transaction network. Subsequently, the behavior features are captured by the Graph Attention Network (GAT) technique in the second classification. Through cascade classification, GasTrace can analyze and classify the sandwich attacks. Our experimental results demonstrate that GasTrace achieves a remarkable detection and generation capability, performing an accuracy of 96.73% and an F1 score of 95.71% for identifying sandwich attack accounts.

Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding \$77 billion. However, detection methods for malicious DeFi events are still lacking. In this paper, we propose DeFiTail, the first framework that utilizes deep learning to detect access control and flash loan exploits that may occur on DeFi. Since the DeFi protocol events involve invocations with multi-account transactions, which requires execution path unification with different contracts. Moreover, to mitigate the impact of mistakes in Control Flow Graph (CFG) connections, we validate the data path by employing the symbolic execution stack. Furthermore, we feed the data paths through our model to achieve the inspection of DeFi protocols. Experimental results indicate that DeFiTail achieves the highest accuracy, with 98.39% in access control and 97.43% in flash loan exploits. DeFiTail also demonstrates an enhanced capability to detect malicious contracts, identifying 86.67% accuracy from the CVE dataset.

Non-Fungible Tokens (NFTs) are digital assets recorded on the blockchain, providing cryptographic proof of ownership over digital or physical items. Although Solana has only begun to gain popularity in recent years, its NFT market has seen substantial transaction volumes. In this paper, we conduct the first systematic research on the characteristics of Solana NFTs from two perspectives: longitudinal measurement and wash trading security audit. We gathered 132,736 Solana NFT from Solscan and analyzed the sales data within these collections. Investigating users' economic activity and NFT owner information reveals that the top users in Solana NFT are skewed toward a higher distribution of purchases. Subsequently, we employ the Local Outlier Factor algorithm to conduct a wash trading audit on 2,175 popular Solana NFTs. We discovered that 138 NFT pools are involved in wash trading, with 8 of these NFTs having a wash trading rate exceeding 50%. Fortunately, none of these NFTs have been entirely washed out., Comment: This paper has been accepted by WWW 2024

As emerging digital assets, NFTs are susceptible to anomalous trading behaviors due to the lack of stringent regulatory mechanisms, potentially causing economic losses. In this paper, we conduct the first systematic analysis of four non-fungible tokens (NFT) markets. Specifically, we analyze more than 25 million transactions within these markets, to explore the evolution of wash trade activities. Furthermore, we propose a heuristic algorithm that integrates the network characteristics of transactions with behavioral analysis, to detect wash trading activities in NFT markets. Our findings indicate that NFT markets with incentivized structures exhibit higher proportions of wash trading volume compared to those without incentives. Notably, the LooksRare and X2Y2 markets are detected with wash trading volume proportions as high as 94.5% and 84.2%, respectively., Comment: This paper has been accepted by WWW 2024

With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity methods. Moreover, the paper evaluates five critical aspects of AI-blockchain-integration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AI-blockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy., Comment: 23 pages, 4 figures

DeFi, or Decentralized Finance, is based on a distributed ledger called blockchain technology. Using blockchain, DeFi may customize the execution of predetermined operations between parties. The DeFi system use blockchain technology to execute user transactions, such as lending and exchanging. The total value locked in DeFi decreased from \$200 billion in April 2022 to \$80 billion in July 2022, indicating that security in this area remained problematic. In this paper, we address the deficiency in DeFi security studies. To our best knowledge, our paper is the first to make a systematic analysis of DeFi security. First, we summarize the DeFi-related vulnerabilities in each blockchain layer. Additionally, application-level vulnerabilities are also analyzed. Then we classify and analyze real-world DeFi attacks based on the principles that correlate to the vulnerabilities. In addition, we collect optimization strategies from the data, network, consensus, smart contract, and application layers. And then, we describe the weaknesses and technical approaches they address. On the basis of this comprehensive analysis, we summarize several challenges and possible future directions in DeFi to offer ideas for further research.

Decentralized finance (DeFi) in Ethereum is a financial ecosystem built on the blockchain that has locked over 200 billion USD until April 2022. All transaction information is transparent and open when transacting through the DeFi protocol, which has led to a series of attacks. Several studies have attempted to optimize it from both economic and technical perspectives. However, few works analyze the vulnerabilities and optimizations of the entire DeFi system. In this paper, we first systematically analyze vulnerabilities related to DeFi in Ethereum at several levels, then we investigate real-world attacks. Finally, we summarize the achievements of DeFi optimization and provide some future directions.

As the most popular blockchain that supports smart contracts, there are already more than 296 thousand kinds of cryptocurrencies built on Ethereum. However, not all cryptocurrencies can be controlled by users. For example, some money is permanently locked in wallets' accounts due to attacks. In this paper, we conduct the first systematic investigation on locked cryptocurrencies in Ethereum. In particular, we define three categories of accounts with locked cryptocurrencies and develop a novel tool named CLUE to discover them. Results show that there are more than 216 million dollars value of cryptocurrencies locked in Ethereum. We also analyze the reasons (i.e., attacks/behaviors) why cryptocurrencies are locked. Because the locked cryptocurrencies can never be controlled by users, avoid interacting with the accounts discovered by CLUE and repeating the same mistakes again can help users to save money., Comment: In Proceedings of the 36th ACM/SIGAPP Symposium on Applied Computing

Being the most popular permissionless blockchain that supports smart contracts, Ethereum allows any user to create accounts on it. However, not all accounts matter. For example, the accounts due to attacks can be removed. In this paper, we conduct the first investigation on erasable accounts that can be removed to save system resources and even users' money (i.e., ETH or gas). In particular, we propose and develop a novel tool named GLASER, which analyzes the State DataBase of Ethereum to discover five kinds of erasable accounts. The experimental results show that GLASER can accurately reveal 508,482 erasable accounts and these accounts lead to users wasting more than 106 million dollars. GLASER can help stop further economic loss caused by these detected accounts. Moreover, GLASER characterizes the attacks/behaviors related to detected erasable accounts through graph analysis., Comment: In Proceedings of the 23rd Information Security Conference (ISC)

More than eight million smart contracts have been deployed into Ethereum, which is the most popular blockchain that supports smart contract. However, less than 1% of deployed smart contracts are open-source, and it is difficult for users to understand the functionality and internal mechanism of those closed-source contracts. Although a few decompilers for smart contracts have been recently proposed, it is still not easy for users to grasp the semantic information of the contract, not to mention the potential misleading due to decompilation errors. In this paper, we propose the first system named STAN to generate descriptions for the bytecodes of smart contracts to help users comprehend them. In particular, for each interface in a smart contract, STAN can generate four categories of descriptions, including functionality description, usage description, behavior description, and payment description, by leveraging symbolic execution and NLP (Natural Language Processing) techniques. Extensive experiments show that STAN can generate adequate, accurate, and readable descriptions for contract's bytecodes, which have practical value for users., Comment: In Proc. of the 20th IEEE International Conference on Software Quality, Reliability and Security (QRS), 2020

Since its inception, the blockchain technology has shown promising application prospects. From the initial cryptocurrency to the current smart contract, blockchain has been applied to many fields. Although there are some studies on the security and privacy issues of blockchain, there lacks a systematic examination on the security of blockchain systems. In this paper, we conduct a systematic study on the security threats to blockchain and survey the corresponding real attacks by examining popular blockchain systems. We also review the security enhancement solutions for blockchain, which could be used in the development of various blockchain systems, and suggest some future directions to stir research efforts into this area., Comment: Published in Journal: Future Generation Computer Systems (2020)

The gas mechanism in Ethereum charges the execution of every operation to ensure that smart contracts running in EVM (Ethereum Virtual Machine) will be eventually terminated. Failing to properly set the gas costs of EVM operations allows attackers to launch DoS attacks on Ethereum. Although Ethereum recently adjusted the gas costs of EVM operations to defend against known DoS attacks, it remains unknown whether the new setting is proper and how to configure it to defend against unknown DoS attacks. In this paper, we make the first step to address this challenging issue by first proposing an emulation-based framework to automatically measure the resource consumptions of EVM operations. The results reveal that Ethereum's new setting is still not proper. Moreover, we obtain an insight that there may always exist exploitable under-priced operations if the cost is fixed. Hence, we propose a novel gas cost mechanism, which dynamically adjusts the costs of EVM operations according to the number of executions, to thwart DoS attacks. This method punishes the operations that are executed much more frequently than before and lead to high gas costs. To make our solution flexible and secure and avoid frequent update of Ethereum client, we design a special smart contract that collaborates with the updated EVM for dynamic parameter adjustment. Experimental results demonstrate that our method can effectively thwart both known and unknown DoS attacks with flexible parameter settings. Moreover, our method only introduces negligible additional gas consumption for benign users.