The burgeoning prevalence of Ethereum phishing behavior has iCSUR-2025-0155mposed substantial constraints on the advancement of blockchain finance, resulting in losses of more than $7.7 billion to date, so it is urgent to detect it in time. Currently, available detection methods usually focus on the spatial features within transaction graphs. These methods often employ shallow mining techniques on small samples. As a result, they may overlook certain aspects of interaction patterns, such as temporal behavior. Additionally, their data mining capability is limited due to the small sample sizes. In this study, we propose a graph contrastive learning framework to enrich features of accounts behavior patterns with restricted samples to overcome these limitations. Firstly, we construct an Ethereum interaction graph with the multi-graph involving more temporal information centered with labeled nodes and lighten it with our strategy. Secondly, to comprehensively characterize the accounts pattern, we design the encoder part with the GAT-LSTM model based on attention mechanism fusing statistical features , fine-grained temporal behavioral features and graph structural semantic features . Thirdly, to moderate the sparsity of phishing nodes, we employ data augmentation and contrastive learning to fully mine sparse node information. Moreover, we carried out an in-depth experimental evaluation. The CMD-EPD approach, boasting an F 1 -score of 0.87, outperformed all comparison methods. We also executed a thorough case study to analyze phishing accounts phenomenological indicators which back up the superiority of our framework.

Abstract Despite the growing attention on blockchain, phishing activities have surged, particularly on newly established chains. Acknowledging the challenge of limited intelligence in the early stages of new chains, we propose ADA-Spear-an automatic phishing detection model utilizing a dversarial d omain a daptive learning which symbolizes the method’s ability to penetrate various heterogeneous blockchains for phishing detection. The model effectively identifies phishing behavior in new chains with limited reliable labels, addressing challenges such as significant distribution drift, low attribute overlap, and limited inter-chain connections. Our approach includes a subgraph construction strategy to align heterogeneous chains, a layered deep learning encoder capturing both temporal and spatial information, and integrated adversarial domain adaptive learning in end-to-end model training. Validation in Ethereum, Bitcoin, and EOSIO environments demonstrates ADA-Spear’s effectiveness, achieving an average F1 score of 77.41 on new chains after knowledge transfer, surpassing existing detection methods.

Abstract Ethereum’s high attention, rich business, certain anonymity, and untraceability have attracted a group of attackers. Cybercrime on it has become increasingly rampant, among which scam behavior is convenient, cryptic, antagonistic and resulting in large economic losses. So we consider the scam behavior on Ethereum and investigate it at the node interaction level. Based on the life cycle and risk identification points we found, we propose an automatic detection model named Aparecium. First, a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors. Second, the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors. Conducting experiments in the wild Ethereum datasets, we prove Aparecium is effective which the precision, recall and F1-score achieve at 0.977, 0.957 and 0.967 respectively.

This article proposes a competitive-evolving-committee proactive secret sharing. Every participant in the system has the opportunity to become a member of the holding committee and have sufficient anonymity. During the life cycle of serving as the holding committee members, they only send one message in the protocol without excessive interaction, and achieve receiver strong anonymity with a capability-based encryption scheme different from most public-key encryption schemes, at present named RiddleEncryption, which is also proposed in this paper. In RiddleEncryption the sender does not need to pay attention to the specific identity of the receiver but focuses on what kind of capability the receiver should have. Nobody can determine this kind of capability at the beginning of the system establishment. This article aims at depositing a secret in a distributed manner (e.g., blockchain) without excessive trust and to emphasize more anonymity and capability. The scheme can be used in the dynamic groups, authentication management, rights abuse prevention, and so on.

Abstract With the wide application and development of blockchain technology in various fields such as finance, government affairs and medical care, security incidents occur frequently on it, which brings great threats to users’ assets and information. Many researchers have worked on blockchain abnormal behavior awareness in respond to these threats. We summarize respectively the existing public blockchain and consortium blockchain abnormal behavior awareness methods and ideas in detail as the difference between the two types of blockchain. At the same time, we summarize and analyze the existing data sets related to mainstream blockchain security, and finally discuss possible future research directions. Therefore, this work can provide a reference for blockchain security awareness research.

The emergence of user-operated media motivates the explosive growth of online videos. Browsing these large amounts of videos is time-consuming and tedious, which makes finding the moments of user major or special preference (i.e. highlights extraction) becomes an urgent problem. Moreover, the user subjectivity over a video makes no fixed extraction meets all user preferences. This paper addresses these problems by posing a query-related highlight extraction framework which optimizes selected frames to both semantically query-related and visually representative of the entire video. Under this framework, relevance between the query text and the video frames is first computed on a visual-semantic feature embedding space induced by a convolutional neural network (Query-Inception network). Then we enforce the diversity on the video frames with the determinantal point process (DPP), a recently introduced probabilistic model for diverse subset selection. The experimental results show that our query-related highlight extraction method is particularly useful for news videos content fetching, e.g. showing the abstraction of the entire video while playing focus on the parts that matches the user queries.