The innovative smart contract mechanism underlying Ethereum has accelerated the growth of its ecosystem. However, the financial transaction characteristics of Ethereum have also facilitated illegal activities, such as phishing scams. These scams pose a serious threat to the security of the ecosystem. Current phishing scam detection methods on Ethereum rely on transaction network graph structures that struggle to capture the complex behavioral patterns of accounts, resulting in low detection efficiency. To address this challenge, this paper proposes a multi-view hypergraph fusion (MVHGF) framework designed to model the distinctive characteristics of Ethereum transaction networks. We design four hypergraph generation strategies from both local (account transaction behavior and transaction motif substructures) and global (transaction network topology and account attributes) perspectives, thereby modeling complex behavioral patterns. Subsequently, hypergraph convolutional neural networks are employed to learn a representation vector for each hypergraph. An adaptive integration mechanism is then used to fuse the multi-view representations, thereby integrating key features from different perspectives. Experimental results show that MVHGF significantly outperforms the single-view graph learning baseline TSGN, improving the AUC from 92.36% to 96.42% and the F1-score from 91.12% to 96.23%, demonstrating excellent detection accuracy and robustness. This work provides a novel approach to detecting phishing scam accounts on Ethereum by effectively fusing multi-view information.

The rapidly expanding Ethereum ecosystem has driven the flourishing of decentralized applications, but has also brought increasingly severe security risks. Ponzi scheme, in particular, pose a grave threat to platform security and user assets by luring investors with promises of high returns. The current detection methods generally suffer from limitations such as insufficient feature extraction, reliance on a single information source, and poor robustness. To address these challenges, this paper proposes a novel Multi-View Multi-Modal Fusion Framework with Large Language Models for Ponzi scheme detection on Ethereum, named MF2LLM. We first model the contract opcode sequence as an opcode chain graph and design a Time-Stamped Graph Encoder (TS-GE) to capture local temporal dependencies and execution flow relationships between opcodes. Concurrently, we construct an opcode semantic hypergraph based on semantic categories and design a Semantic-Weighted Hypergraph Encoder (SW-HGE) to model higher-order co-occurrence patterns and global associative features. Furthermore, we propose the Opcode Sequence Lightweighting (OSL) method, which significantly compresses the length of opcode sequences while preserving core control logic and semantic information. This provides high-quality structured input for information fusion. To this end, we perform multi-modal instruction fusion on multi-source heterogeneous features and employ LoRA to fine-tune LLMs. This enables the model to achieve cross-modal semantic reasoning and behavioural pattern recognition. Through extensive experimental validation on real-world datasets, MF2LLM demonstrates stable and superior detection performance even under conditions of highly imbalanced sample distributions. Compared to existing state-of-the-art approaches, our method outperforms across all metrics, achieving an ACC of 99.43%, Precision of 96.57%, Recall of 97.06%, and an F1-score of 96.81%. The efficiency and practical value of MF2LLM in detecting Ponzi schemes on Ethereum contribute to enhanced security for the decentralized application ecosystem. The codes are publicly available on Github: https://github.com/yemisua/MF2LLM.

The rapid development of the Ethereum platform has been accompanied by increasingly severe security challenges, among which phishing attacks have become one of the most prevalent attack vectors. Attackers exploit the anonymity of blockchain accounts and employ social engineering techniques to induce users to disclose private keys or grant authorizations to malicious smart contracts, resulting in substantial economic losses. Current detection methods struggle to simultaneously capture dynamic temporal patterns, higher-order interactions, and long-range dependencies in transaction networks. To address these challenges, this study proposes an approach that integrates evolving Hypergraph Sequence with Large Language Models for detecting phishing scam accounts on Ethereum, named HyperSeq-LLM. Specifically, we employ a sliding window to construct temporal transaction sequences that capture dynamic transaction patterns, and we develop a hypergraph generation method based on feature clustering to model multi-party higher-order interactions within these sequences. Additionally, we integrate hyperedge-size-aware weighting into Hypergraph Convolutional Networks (AwareHGCN) to enhance the representation of groups. Finally, we employ quantized low-rank adaptation techniques to fine-tune large language models to capture the evolution of account transaction patterns within temporal hypergraphs. Experiments on the Ether@P1:N1 and Ether@P1:N10 datasets demonstrate that HyperSeq-LLM consistently surpasses state-of-the-art baselines, exhibiting enhanced detection accuracy and early-warning capabilities. These findings underscore the efficacy of integrating dynamic modeling with multi-relational interactions and highlight HyperSeq-LLM’s scalability and practical potential for securing the Ethereum ecosystem. The codes are publicly available on Github: https://github.com/yemisua/HyperSeq-LLM

Smart contract vulnerability detection has been more and more important in recent years. By the economic nature of blockchain, the vulnerabilities in smart contracts may directly lead to large financial losses. A variety of detection methods have been proposed to detect vulnerabilities such as reentrancy and timestamp dependence. However, as the number of smart contracts rapidly increases, new forms of reentrancy vulnerability have been exploited to perform powerful attacks. Existing machine learning based detection methods build their model based on the traditional “callvalue” pattern to detect reentrancy. Thus they are not able to adapt to the newly emerged vulnerabilities. In this work, we take the first step into building a flexible and accurate machine learning model for detecting the newly emerged vulnerabilities such as the “callvalue-excluded” reentrancy. Based on a deep investigation into real-world smart contracts, we extract effective features from program syntax and semantics, i.e. customized code property graph. A detection model named Code Property Graph Neural Network (CPGNN) is proposed to detect vulnerabilities in smart contracts. The proposed method is evaluated on over 2,000 smart contracts and the experiment results show that CPGNN can achieve high efficiency in vulnerability detection problem while effectively improving the detection accuracy.

The emergence of Ethereum has created new opportunities for decentralized finance. However, the increasing prevalence of illegal activities, such as phishing scams, poses a significant threat to the healthy development of the Ethereum ecosystem. To address this issue, this study proposes an approach that leverages Temporally Evolving Graph Sequences for Detecting Phishing Scams on Ethereum, named GraphSeqGuard. Specifically, we design a novel algorithm to generate dynamically ordered transaction subgraph sequences for each account. To this end, we employ graph convolutional networks as encoders to extract subgraph embedding representations and present local and global subgraph sequence contrastive learning to train the encoder, thereby enhancing its learning capabilities. Additionally, we combine a bidirectional long short-term memory network with a deep state-space module and a multi-head differential attention mechanism to develop a classifier for detecting phishing scam accounts. Evaluated on a real Ethereum dataset, GraphSeqGuard outperforms state-of-the-art approaches in detecting phishing accounts. The proposed approach demonstrates clear advantages in capturing transaction patterns and tracking their dynamic evolution, offering a robust solution for enhancing the security of Ethereum transactions.

Blockchain technology is celebrated for its transparency and immutability, revolutionizing trust models. However, its decentralized nature raises concerns about potential inclusion of malicious or illegal content. This study focuses on Ethereum’s blockchain, proposing an algorithm for data identification and restoration. We successfully recovered 175 files, 296 images, and 91,206 texts. Employing FastText for sentiment analysis, we achieved 0.9 accuracy after parameter tuning. Classification revealed 70,189 neutral, 5,208 positive, and 15,810 negative texts, aiding in identifying sensitive or illicit information. Our findings expose benign and harmful content coexisting on Ethereum, including personal data, explicit images, divisive language, and racial discrimination, notably targeting Chinese government officials. This study provides valuable insights for public understanding and regulatory guidance on blockchain technology.

With the burgeoning adoption of blockchain technology, cryptocurrencies have surged in popularity, becoming a focal point of global interest. Concurrently, the emergence of cryptocurrency phishing scams poses a significant threat to the financial security of the blockchain ecosystem, inflicting substantial economic damage on platforms and users alike. This study introduces an innovative approach leveraging an attention-augmented Bidirectional Long Short Term Memory Network (BiLSTM), termed BiLSTM4DPS, for the detection of phishing scams within the Ethereum network. We initiate by converting account transaction records into sequences, thereby extracting temporal and latent patterns of transactions. Subsequently, we integrate BiLSTM with multi-head attention mechanisms and masking techniques to construct a robust classification model aimed at identifying fraudulent accounts. Extensive experiments were conducted to assess the efficacy of BiLSTM4DPS, particularly under scenarios with limited account activity data. The results demonstrate that BiLSTM4DPS achieves remarkable predictive accuracy, surpassing existing state-of-the-art methods.

Semantic code search, retrieving code that matches a given natural language query, is an important task to improve productivity in software engineering. Existing code search datasets face limitations: they rely on human annotators who assess code primarily through semantic understanding rather than functional verification, leading to potential inaccuracies and scalability issues. Additionally, current evaluation metrics often overlook the multi-choice nature of code search. This paper introduces CoSQA+, pairing high-quality queries from CoSQA with multiple suitable codes. We develop an automated pipeline featuring multiple model-based candidate selections and the novel test-driven agent annotation system. Among a single Large Language Model (LLM) annotator and Python expert annotators (without test-based verification), agents leverage test-based verification and achieve the highest accuracy of 93.9

The birth of Bitcoin created the era of cryptocurrency. At present, thousands of tokens are being published on blockchains, led by the popularity of Initial Coin Offerings (ICOs), launched by startups. Many have studied the token ecosystem from the perspective of economics. However, little is known about the characteristics of participants and tokens in the ecosystem. In order to fill this gap, and considering that more than 80% of ICOs are launched based on Ethereum, this article conducts a systematic survey of the Ethereum ERC20 and ERC721 token ecosystem to characterize the creators and holders of the tokens and transfer activities. By downloading the entire blockchain, and analyzing transaction records and event logs, we constructed three complex network graphs, a token creator graph, a token holder graph, and a token transfer graph. By analyzing these graphs, we are able to obtain many observations including token activeness distribution, token holder distribution, token relationship, and characteristics of token creation and decentralized exchanges. In addition, we propose an algorithm to reveal potential relationships between tokens and other addresses on Ethereum. The example shows that the proposed algorithm can effectively discover complex relationships between many accounts and find entities of addresses.

With the rapid advancement of blockchain technology, cryptocurrencies based on blockchain have become a hot topic. However, various issues accompany this development, with phishing scams emerging as a severe financial crime within the blockchain ecosystem, causing significant economic losses to both blockchain platforms and users. In order to address this threat, this essay proposes a phishing scam account identification model based on Bidirectional Long Short-Term Memory Networks (BiLSTM) named BILAM. The model has been validated on the Ethereum platform and has been proven to be effective.This study proposes a novel approach by using transaction records for the first time to construct a time series, and it leverages the BILAM model to learn latent information. Experimental results demonstrate the effectiveness of this method in constructing transaction time series. Moreover, the BILAM model shows excellent performance, with its predictive accuracy significantly surpassing other models, particularly achieving an AUC index of 92.8%.

Malware detection approaches have been extensively studied for traditional software systems. However, the development of blockchain technology has promoted the birth of a new type of software system–decentralized applications. Composed of smart contracts, a type of application that implements the Ponzi scheme logic (called smart Ponzi schemes) has caused irreversible loss and hindered the development of blockchain technology. These smart contracts generally had a short life but involved a large amount of money. Whereas identification of these Ponzi schemes before causing financial loss has been significantly important, existing methods suffer from three main deficiencies, i.e., the insufficient dataset, the reliance on the transaction records, and the low accuracy. In this study, we first build a larger dataset. Then, a large number of features from multiple views, including bytecode, semantic, and developers, are extracted. These features are independent of the transaction records. Furthermore, we leveraged machine learning methods to build our identification model, i.e., Multi-view Cascade Ensemble model (MulCas). The experiment results show that MulCas can achieve higher performance and robustness in the scope of our dataset. Most importantly, the proposed method can identify smart Ponzi scheme at the creation time.

Spatio-temporal traffic data collected by various sensing systems have chronic issues of missing and corruption, thus accurate data imputation and prediction have been extensively researched. Previous methods can be divided into two categories: model-driven methods with physical explanations and data-driven deep learning methods. These methods have some limitations: model-driven methods are often linear and may not accurately model the spatio-temporal complexity, while deep learning methods often lack the physical reality making them probe to over-fitting. Inspired by both, we propose a new Convolutional-based generalized Autoregressive Tensor-Ring decomposition method (CoATR) for the completion of spatiotemporal data. CoATR not only retains the advantages of the tensor-ring (TR) decomposition model for global modeling of spatio-temporal data but also exploits the ability of deep networks to model nonlinear features. To be specific, we introduce TR decomposition to capture the global low-rankness and employ multilayer convolutional neural networks to model the global complex interactions among the TR factors. Moreover, we design a new autoregressive network to further explore the local temporal variation in the data. Extensive experiments on a variety of common traffic datasets have validated the effectiveness and superiority of the CoATR over classical model-driven methods and other state-of-the-art data-driven deep learning methods.

Cryptocurrency is a new type of digital currency that utilizes blockchain technology and cryptography to achieve transparency, decentralization, and immutability. Bitcoin became the world’s first decentralized cryptocurrency in 2009. With increasing attention given to cryptocurrency, predicting its price has become a popular research topic. Many machine learning and deep learning algorithms, such as Gated Recurrent Unit (GRU), Neural Network (NN), and Long Short-Term Memory (LSTM), have been studied for cryptocurrency price prediction. In this paper, we propose a hybrid cryptocurrency price prediction model based on LSTM and GRU. The model achieves better results than the LSTM model in cryptocurrency price prediction and performs the best among existing hybrid models based on LSTM and GRU.

With the popularity of Non-Fungible Tokens (NFTs), which has now become a financial market that has attracted extensive attention worldwide. A large number of investors and creators are flocking to this emerging market in search of investment opportunities. Nowadays, many studies have analysed this phenomenon from an economic perspective. However, we know little about the players and ecosystem characteristics of this market. To fill this knowledge gap, we first provide a processed large-scale dataset of the Ethereum blockchain-based NFT market, containing more than 80 million NFT transaction records from January 2018 to April 2022. Second, we constructed the NFT creator graph (NCG) and NFT holder graph (THG) to delve into the characteristics of the NFT market. Further, we analyse the market preferences and trends using statistical methods to reveal the development trends of the NFT market. Finally, we focus on predicting the transaction volume of the NFT market and analyse the influence factors. This study provides data support for participants and researchers to explore the NFT market, while our analysis promotes a deeper understanding of the NFT market among the public.

Blockchain technology has created a new cryptocurrency world and attracted a lot of attention. It also attracts scams, for example, phishing scam, a typical fraud, has been found making a notable amount of money in the blockchain ecosystem, which has a very negative impact. Considering the whole life cycle of a phishing scam, this paper proposes the concept of a phishing gang, that is, a set of accounts that serve for phishing activity and belong to the same entity on the blockchain. As phishers often use multiple accounts to commit phishing scams and money laundering, detecting phishing gangs in the blockchain ecosystem is a real and critical problem. To help deal with this issue, this paper proposes a method of detecting phishing gangs on the Ethereum blockchain. Specifically, we first construct a transaction network with a graph structure by mining the transaction record and the account labels of the Ethereum blockchain. Next, we propose the base and improvement methods of taint analysis, aiming to evaluate the taint score of each account by tracking the fund flow of phishing accounts. Then, with the results of taint analysis and some heuristic means, all accounts in the transaction network are divided into five categories. Based on this, we propose a heuristics algorithm for phishing gang detection. And we also summarize gang patterns and reveal money laundering in phishing activities. Experimental results indicate that the proposed framework can be used to build a uniform platform to monitor every account on the Ethereum blockchain for early warning of phishing scams and detection of the phishers' money laundering and cashing process.

The ceramic supply chain is a complex network centered on ceramic products, encompassing business flow, logistics, information flow, and capital flow, and involving many participants and complex business processes. Currently, the key issues confronting the ceramic supply chain are lack of trust between different participants, financing difficulty for medium, small, and micro enterprises, inefficient transportation and distribution, and the inability to meet the diversified needs of customers. These pain points can be properly sloved by blockchain technology, with characteristics such as immutability and decentralization as well as the application of smart contracts. In this paper, a system architecture of Blockchain-Based Ceramic Supply Chain (BCSChain) is presented, and three applications of BCSChain in different scenarios are proposed, including logistics traceability, financing, and automatic trading. It is found that combing blockchain technology with ceramic supply chain can contribute to enable data sharing and information visualization, de-intermediation and data security, automated execution, as well as efficient collaboration.

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.

The birth of Bitcoin has created the cryptocurrency exchange, the average daily trading volume of cryptocurrency exchanges is now more than 100 billion. Cryptocurrency exchanges serve as a place for users to exchange cryptocurrencies, acting as a bridge between the blockchain ecosystem and the real world. Based on the transaction mechanism, cryptocurrency exchanges can be divided into centralized exchanges(CEXs) and decentralized exchanges(DEXs). CEXs still hold the dominant position, and we focus on Mt.Gox with the leaked dataset. By preprocessing the data, a usable internal dataset was obtained. To better study CEX, we further provide a comprehensive analysis of Mt.Gox based on three types of records and conclude its characteristics. Finally, we propose a matching method for on-chain and off-chain data, which restores the complete transaction path of the transaction account and some strange transaction phenomena are discovered. The results of this experiment showed that our algorithm can find addresses on blockchain and de-anonymize to a certain extent.

As the first decentralized peer-to-peer (P2P) cryptocurrency system allowing people to trade with pseudonymous addresses, Bitcoin has become increasingly popular in recent years. However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. Particularly, mixing services in Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundering to complicate the process of trailing illicit fund. In this article, we focus on the detection of the addresses belonging to mixing services, which is an important task for anti-money laundering in Bitcoin. Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level, and transaction level. To better characterize the transaction patterns of different types of addresses, we propose the concept of attributed temporal heterogeneous motifs (ATH motifs). Moreover, to deal with the issue of imperfect labeling, we tackle the mixing detection task as a positive and unlabeled learning (PU learning) problem and build a detection model by leveraging the considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of our detection model and the importance of hybrid motifs including ATH motifs in mixing detection.