Detecting Ethereum phishing scams is extremely urgent. In this paper, we propose a novel Hybrid Attention Model for Ethereum phishing scams detection called HATTM to solve the problem of irregular transaction series in Ethereum, fully extract account features and then improve detection performance. Specifically, we take a novel perspective by regarding each transaction of an account as a separate amount-time point to handle irregular data. In the hybrid attention model, we capture intra-account and inter-account trading features through intra-account attention of EPS-FORMER and inter-account attention of EPSGAT, respectively. We further extract Intra-account and Inter-account statistical features to enrich the account representation. The complete representation of accounts is composed of the above four types of features to detect phishing accounts. Experimental results on the real-world Ethereum dataset show that HATTM outperforms existing models and is far ahead in the recall, which indicates that our model can effectively detect Ethereum phishing scams.

As a representative of the public blockchain, Ethereum has been applied in various industries. However, the vast number of transactions on the platform has also brought a number of illegal activities, such as phishing scams, which have caused significant damage to the Ethereum ecosystem. Due to anonymity of the blockchain, it is difficult for detectors to extract features that can be directly applied to phishing scams detection. Existing studies mainly model Ethereum transaction records as a network and mine key information from them to identify phishing addresses. However, these methods usually employ traditional feature engineering or network embedding, ignoring the fine-grained features in the transaction network. In addition, since the original network is too large to make learning difficult, existing work usually uses random walk (RW) to sample a part of nodes for training, thus ignoring the multiplicity of the network. To address these issues, in this article, we propose a three-stream feature fusion (TSFF) approach to enhance the feature representation of nodes. Specifically, we construct node states to guide RW sampling, and manually extracted 8-D features from the resulting dataset as basic features. Temporal features are jointly learned through long short-term memory network and contrastive learning. We combine residual blocks and graph convolutional network to extract fine-grained structural features from transactional networks. Finally, we fuse these three types of features and input them into a downstream classifier. Experiments show that our TSFF (85.3% Precision) outperforms the state-of-the-art methods, and the effectiveness of each feature is demonstrated.

In recent years, phishing scams have seriously threatened Ethereum's ecological security and caused massive economic losses. Moreover, the significant disparity between the number of normal addresses and phishing addresses on Ethereum poses a challenge for detecting phishing scams. Existing studies primarily employ methods such as oversampling, filtering rules, and traditional machine learning models to resolve the Ethereum data imbalance problem. However, these methods disregard topological structure features of the transaction network and the link relationship between nodes. In this paper, we propose an Ethereum phishing scams detection model based on Generative Adversarial Graph Networks called EGAGN to alleviate imbalanced data, enhance node representation, and then improve detection performance. Specifically, the graph generator and discriminator play with each other to generate synthetic nodes that satisfy the real nodes distribution to balance Ethereum data and extract effective network structural features. We further extract statistical features from the transaction network and aggregate transaction records based on time series to obtain trading features. The complete representation of nodes is composed of the above three types of features to detect phishing nodes. Experimental results on the real-world Ethereum dataset show that EGAGN outperforms existing models and is far ahead in recall, which indicates that our model can effectively detect Ethereum phishing scams.

Cryptocurrency crime incidents in Ethereum are continuously rising, with phishing scams accounting for 50% of all criminal activities. The severe data imbalance significantly impacts the performance of Ethereum phishing detection models. The current solution may introduce redundant information or lead to the loss of important data. In this paper, we propose an Ethereum phishing detection method based on Graph Contrastive Learning with augmentations. This approach addresses the issue of insufficient learning of phishing node features, thus alleviating the influence of data imbalance on the model’s detection performance without disrupting the original data distribution. To enhance the representation of structural features, we employ two data augmentation methods: feature masking and edge perturbation. We conducted extensive experiments on a real Ethereum phishing dataset to evaluate the performance of our method. Compared to alternative methods, our approach not only significantly improves Precision, ranging from 12% to 30%, but also achieves noticeable enhancements in Recall, Auc, and F1-score. The experimental results provide ample evidence of the effectiveness of the proposed method.

The rise of Ethereum in various economic and social domains has made it a prime target for illegal activities, particularly phishing scams, which has caused substantial financial losses. Existing methods mainly model transaction records as networks and classify nodes. However, a particular challenge is that not all transactions involved in phishing nodes are illegal, which makes phishing detection very difficult. In order to address the problem, in this paper, we propose a Curriculum Learning-based method for Ethereum phishing detection. We incorporate the Local Outlier Factor to measure the difficulty of nodes, considering the significant feature differences among nodes of the same class. By assigning lower difficulty values to easily identifiable nodes and higher difficulty values to nodes involved in mixed-class transactions, we ensure an effective difficulty measure. Then we gradually increase the number of training nodes input into Graph Convolutional Network in each epoch based on a certain ratio determined by the sorted difficulty scores. Finally, we employ LightGBM as the classifier for identifying phishing nodes. Experimental evaluations on a real-world Ethereum phishing scam dataset demonstrate the superiority of our method over baseline approaches, as evidenced by several evaluation metrics.

Phishing scams have become one of the primary frauds on Ethereum, leading to substantial financial losses for users. Therefore, developing an effective phishing detection method for Ethereum holds significant importance for the blockchain ecosystem. However, the heterogeneity of Ethereum poses challenges when it comes to detecting phishing scams. In this paper, we propose a Curriculum Learning-based approach (CLPSD) for Ethereum phishing detection. We collect transaction records to build a graph. In order to distinguish the varying degrees of contribution among nodes, we design a difficulty measurer combining Local Outlier Factor and information entropy. Thus, CLPSD utilizes Graph Convolutional Network to initially learn from easy samples and progressively advance to more complex ones. The experimental results demonstrate that CLPSD outperforms existing methods, highlighting the superior performance of our approach and its significant relevance in enhancing the Ethereum ecosystem.

Blockchain has facilitated the growth of cryptocurrencies but has also provided new ideas for illegals to commit fraud. Research on malicious accounts detection shows that the number of malicious accounts is much smaller than that of benign accounts, leading to imbalanced dataset samples. Most researchers adopt the under-sampling method to help deal with this issue, but this method does not correspond to the actual scale. So, we propose an anomaly detection method based on community discovery. Firstly, we use the transaction information in the Ethereum public chain to build a transaction network and use the Louvain algorithm to divide the transaction network into communities. Secondly, we use the LightGBM algorithm to classify the community. Finally, based on the classification results, we use HBOS, LOF, K-Means, KNN and iForest algorithms as benchmark algorithms for anomaly detection and compare the experimental results using the methods in this paper with the results of anomaly detection using the original transaction network. Experimental show that our method can reduce the amount of data by 35.53% and increase the AUC values of the five algorithms by 7.52%, 8.41%, 14.88%, 0.83% and 27.95%.

Applications in the private Ethereum blockchain environment are required by many enterprises. Although, un-like public Ethereum platforms, private Ethereum platforms allow some specific parameters to be configured at setup time to increase throughput, its throughput performance is still not sufficient for enterprise-level applications due to the low scalability of the Ethereum blockchain. This paper analyzes and tests the throughput of the private Ethereum blockchain at the hardware level, and according to the analysis results, proposes a parallelized dynamic load balancing scheme based on network sharding. On this basis, various charts are created to visually show the impact of changing hardware parameters on performance, and experiments are conducted to verify that the model can further improve the performance of private Ethereum blockchains through effective load balancing.

With the development of blockchain technology, the data on the blockchain represented by Bitcoin and Ethereum is exploding. Data represents people's activities, indicating that blockchain has been paid more and more attention. However, many problems are hidden behind so much data, such as security and privacy. Analyzing the data can help to find out the problems and propose methods to improve blockchain. Therefore, in order to make the blockchain well applied to various walks of life, data analysis is quite essential. In this paper, we collected the relevant literature in the field of blockchain data analysis, summarized the current analysis methods, and analyzed the research status from four aspects: security, privacy, performance and prediction of price. Finally, we discussed those research work and forecasted the future trend and challenges in this field, providing some reference for related researchers. We thought that applying machine learning technology to blockchain data analysis will become mainstream in the future.

In recent years, Vehicular Ad Hoc Network (VANET) has developed significantly. Coordination between vehicles can enhance driving safety and improve traffic efficiency. Due to the high dynamic characteristic of VANET, security has become one of the challenging problems. Trust of the message is a key element of security in VANET. This paper proposes a Manhattan Distance Based Trust Management model (MDBTM) in VANET environment which solves the problem in existing trust management research that considers the distance between the sending vehicle and event location. In this model, the Manhattan distance and the number of building obstacles are calculated by considering the movement relationship between the sending vehicle and event location. The Dijkstra algorithm is used to predict the path with the maximum probability, when the vehicle is driving toward the event location. The message scores are then calculated based on the Manhattan distance and the number of building obstacles. Finally, the scores are fused to determine whether to trust the message. The experimental results show that the proposed method has better performance than similar methods in terms of correct decision probability under different proportions of malicious vehicles, different numbers of vehicles, and different reference ranges.

With the development of wireless communication technology and the automobile industry, the Vehicular Ad Hoc Networks bring many conveniences to humans in terms of safety and entertainment. In the process of communication between the nodes, security problems are the main concerns. Blockchain is a decentralized distributed technology used in nonsecure environments. Using blockchain technology in the VANETs can solve the security problems. However, the characteristics of highly dynamic and resource-constrained VANETs make the traditional chain blockchain system not suitable for actual VANETs scenarios. Therefore, this paper proposes a lightweight blockchain architecture using DAG-lattice structure for VANETs, called V-Lattice. In V-Lattice, each node (vehicle or roadside unit) has its own account chain. The transactions they generated can be added to the blockchain asynchronously and parallelly, and resource-constrained vehicles can store the pruned blockchain and execute blockchain related operations normally. At the same time, in order to encourage more nodes to participate in the blockchain, a reputation-based incentive mechanism is introduced in V-Lattice. This paper uses Colored Petri Nets to verify the security of the architecture and verifies the feasibility of PoW anti-spam through experiment. The validation results show that the architecture proposed in this paper is security, and it is feasible to prevent nodes from generating malicious behaviors by using PoW anti-spam.