Frequent security issues with smart contract vulnerabilities have become a pressing challenge in the industry. Conventional program analysis methods lack flexibility and extensibility, leading to high false positive rates. Deep learning approaches are emerging as a new trend to address this issue. Compared to other neural networks, graph convolutional networks can better capture the structural and logical information of smart contracts. However, existing methods do not fully consider the scale-free characteristics of smart contracts and fail to leverage their complex hierarchical structures and semantic information. Therefore, we develop an end-to-end vulnerability detection framework using Riemannian Graph Convolutional Networks (RGCNet). We first construct smart contract graphs that are rich in semantic and structural information. Next, we learn features of the smart contract graph in the Riemannian manifold, thereby better reflecting its actual topology. Simultaneously, the word embedding network extracts semantic features, forming an end-to-end network where modules promote one another. Extensive experiments are conducted on three vulnerabilities using real-world smart contracts. The results show that the proposed approach exhibits superior performance over state-of-the-art methodologies in terms of accuracy, precision, and recall.
Federated Learning (FL) has emerged as a transformative paradigm for distributed machine learning, enabling model training across decentralized edge devices while preserving data privacy. This methodology is critical for sectors handling sensitive information, such as finance, healthcare, and the Internet of Things (IoT). Despite its benefits, the coordination and communication overhead between distributed nodes remain significant challenges. This paper evaluates the efficacy of REST and GraphQL API architectures in facilitating FL workflows. While REST APIs are favored for their statelessness and simplicity, GraphQL offers enhanced flexibility and efficiency by enabling precise data fetching—a vital feature for bandwidth-constrained decentralized systems. We provide a comparative analysis of these paradigms across performance, security, and scalability metrics, specifically regarding data synchronization and model aggregation. Finally, we propose design best practices for developing APIs that support robust, compliant, and efficient federated prediction systems.
Chi Jiang, Caixing Shao, Xiaoyan Huang, Ming Tao · 6 authors
Blockchain technology is evolving toward next-generation blockchain systems, where AI-empowered optimization plays a key role in enhancing scalability, efficiency, and security. However, in the context of smart contract vulnerability detection, however, most vulnerabilities originate from a few critical code fragments, resulting in a severe signal-to-noise imbalance. This imbalance introduces representation noise and redundant information in complex contract code, obscuring the true vulnerability semantics and hindering the effectiveness of AI-based models such as graph neural networks (GNNs). To address this challenge, we propose CoTA, a feature augmentation framework that leverages two easily obtainable signals: code-cohort feature and task-shared feature. Code-cohort features capture the observation that contracts with similar structural/behavioral patterns often implement similar business functions, and thus share vulnerability priors (e.g., higher reentrancy risk in fund-transfer-heavy cohorts). Task-shared feature, on the other hand, learned via a multi-task setup that jointly trains coarse contract-level multi-label classification and fine-grained function-level labeling to distill shared representations. Extensive experiments on real-world dataset show that CoTA achieves consistent improvements in detection accuracy, offering a practical and effective solution for smart contract security analysis.
Graph neural networks (GNNs) have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control- and data-flow graphs. Despite their eff... | Find, read and cite all the research you need on Tech Science Press
The rise of Bitcoin has revolutionized the financial landscape, but it has also opened the door to a new era of criminal activities. Criminals take advantage of the anonymity provided by Bitcoin to conduct illicit transactions and engage in fraudulent activities. To address this issue, this paper proposes a detection model using Graph Neural Networks (GNNs) to detect fraudulent activities in the complex financial systems of Bitcoin. From the GNNs, we use EvolveGCN and EvolveGGCN to compare between them and find a powerful model that can investigate the network construction of financial transactions and capture patterns and anomalies that traditional methods may miss. In the literature, there have been a limited number of studies on Bitcoin fraud detection using GNNs, especially EvolveGGCN. Therefore, in this paper, we focus on the detection of fraud in the Bitcoin network using EvolveGCN and EvolveGGCN. In addition, we used a more recent dataset called Elliptic++, which is an extension of the Elliptic Dataset. The dataset provides valuable information on the behavior and patterns of fraudulent actions in the Bitcoin network. The results show that EvolveGGCN outperforms other models in terms of precision, recall, F1 score, and micro-F1 score. With an F1-score of 0.90 and micro-F1 of 0.93 for detecting illicit transactions in the early time steps.
Illicit activities and coordinated manipulations in the Non-Fungible Tokens (NFT) market remain significant concerns, driven by the pseudonymous and publicly transparent nature of blockchain transactions and the lack of market oversight. In this study, we introduce a novel framework for detecting suspicious behavior in NFT trading ecosystems through temporal graph analysis. Our approach represents the market as a large-scale, time-evolving transactional graph, capturing realistic market dynamics and the complex interactions between traders.By leveraging temporal graphs, we track not only connections between traders but also the evolution of these interactions over time, including their sequence, rhythm, and frequency, enabling the identification of anomalous behaviors that static graph representations cannot reveal and that may warrant further examination. Using an optimized temporal cycle-detection algorithm, we extract connected groups of wallets for in-depth behavioral analysis, uncovering patterns indicative of unusual coordinated manipulation. To overcome the absence of labeled ground-truth validation data, we employ a temporal motif–based validation, demonstrating that flagged entities exhibit trading behaviors significantly deviating from standard market dynamics. Our results highlight the potential of temporal graph–based methodologies to provide a scalable and effective risk-profiling and market-surveillance tool, assisting analysts and regulatory entities in narrowing the investigation space within the large, permissionless NFT markets and enhancing surveillance, risk detection, and regulatory oversight in decentralized NFT markets.
Alex J. Sinclair, Yuhan Zhang, Michael P. O’Connor, Li Wei
Graph Neural Networks (GNNs) have become the de facto standard for modeling blockchain transaction networks. However, standard GNN architectures predominantly operate under the assumption of homophily-that connected nodes share similar labels or features. This assumption catastrophically fails in the context of Ethereum phishing detection, a quintessential heterophilic learning problem where fraudsters (phishing accounts) actively connect with unlike victims (normal accounts) to facilitate theft. In this work, we argue that the "homophily bottleneck" in existing detection systems obscures critical high-frequency signals necessary for identifying illicit activity. We introduce the Heterophilic Semantic Graph Framework (HSGF), a novel architecture designed to decouple structural roles from feature smoothing. HSGF integrates a Motif-based Semantic Sampling (MSS) strategy to capture complex, directed transactional intents (e.g., dispersing, gathering, mixing) and a Heterophily-Aware Feature Fusion (HAFF) module that prevents the oversmoothing of fraudster representations into victim representations. Extensive experiments on real-world Ethereum datasets demonstrate that HSGF significantly outperforms state-of-the-art baselines, particularly in class-imbalanced scenarios, effectively breaking the ceiling imposed by traditional homophilic aggregation.
Ye Tian, Liangliang Song, Yuanyuan Ma, Yanbin Wang · 7 authors
Detecting fraudulent accounts on Ethereum is critical for securing decentralized finance (DeFi) ecosystems. Although temporal transaction dynamics offer richer behavioral signals than static graphs, existing methods struggle to jointly model the continuous, directional, and community-level nature of fraudulent fund flows. Specifically, they inadequately capture two key cues: directional-temporal transaction patterns (integrating directionality and temporal dynamics) and higher-level behavioral communities formed by accounts with similar transactional and temporal traits.To address these, we propose TimeTrace, an unsupervised graph representation learning framework for fraud detection in Ethereum transaction networks. TimeTrace models temporal behaviors via a Directed Temporal Aggregation mechanism that explicitly distinguishes incoming and outgoing flows while adaptively emphasizing recent interactions to capture pattern evolution. Building on these directional representations, TimeTrace further incorporates a differentiable clustering module to identify latent behavioral communities and encode cluster-level relational semantics. To enhance representation coherence and structural consistency, we introduce Cohesive Embedding Regularization (CER)—a graph refinement objective combining a global Laplacian term for structural consistency, an intra-cluster Laplacian term for cluster compactness, and a fidelity term to retain initial clustering information. Additionally, we construct a new phishing account benchmark with up-to-date Ethereum transaction records for realistic evaluation. Extensive experiments on three datasets demonstrate that TimeTrace outperforms state-of-the-art methods in both accuracy and efficiency, achieving F1-score improvements of 3.62\%-10.83\% and inference speeds at least as fast as the quickest baseline.
With the rapid advancement of Web 3.0 technologies, public blockchain platforms are witnessing the emergence of novel services designed to enhance user privacy and anonymity. However, the powerful untraceability features inherent in these services inadvertently make them attractive tools for criminals seeking to launder illicit funds. Notably, existing de-anonymization methods face three major challenges when dealing with such transactions: highly homogenized transactional semantics, limited ability to model temporal discontinuities, and insufficient consideration of structural sparsity in account association graphs. To address these, we propose GradWATCH, designed to track anonymous accounts in Ethereum privacy-preserving services. Specifically, we first design a learnable account feature mapping module to extract informative transactional semantics from raw on-chain data. We then incorporate transaction relations into the account association graph to alleviate the adverse effects of structural sparsity. To capture temporal evolution, we further propose an edge-aware sliding-window mechanism that propagates and updates gradients at three granularities. Finally, we identify accounts controlled by the same entity by measuring their embedding distances in the learned representation space. Experimental results show that even under the conditions of unbalanced labels and sparse transactions, GradWATCH still achieves significant performance gains, with relative improvements ranging from 1.62% to 15. 22% in the MRR and from 3. 85% to 7. 31% in the F_1.
Non-fungible tokens (NFTs) have become a key asset class in Web3 markets, where visual artwork, textual narratives, and on-chain transaction patterns jointly determine value, yet their pricing dynamics remain volatile, opaque, and difficult to explain. Existing NFT valuation methods typically either ignore the multimodal nature of NFTs or treat assets as independent samples, failing to exploit the rich relational structures induced by shared creators, collections, and ownership patterns, and offering limited interpretability for high-stakes financial decisions. To address these challenges, we propose NFT-Insight, a multimodal graph transformer framework that unifies visual, textual, and blockchain information on a heterogeneous NFT graph and explicitly links structural and content signals to valuation behavior. The framework identifies closely related NFTs via a joint similarity measure in the multimodal embedding space, propagates information through a relation-specific graph attention network and a global transformer encoder, and adopts a regularization strategy that encourages consistent valuations for highly similar assets while still allowing data-driven differentiation. In addition, NFT-Insight integrates attention-based and SHAP-based explanations into a unified analysis pipeline, enabling joint study of valuation behavior and feature attributions at the level of related NFT pairs. Experiments on three large-scale, real-world NFT datasets show that NFT-Insight consistently outperforms strong unimodal, multimodal, and graph-based baselines, reducing MAE and RMSE by up to about 20% in static valuation (withR2up to 0.904), achieving robust cross-market performance with averageR2≈ 0.84 andr≈ 0.93, and attainingR2= 0.911 in temporal forecasting. Interpretability analysis reveals that visual, textual, blockchain, and graph-relational features achieve a high alignment between SHAP importance and attention weights (average Spearman correlation above 0.8), and case studies highlight meaningful valuation patterns driven by rarity, speculative trading, and temporal market shocks. Overall, the proposed framework offers a multimodal graph–based perspective on explainable NFT valuation and market forecasting, and provides a general template for incorporating complex relational and content interactions into graph-based learning in decentralized digital economies.
While Decentralized Autonomous Organizations (DAOs) and Artificial Intelligence are reshaping the governance of academic societies, reliably integrating on-chain decisions with off-chain physical activities remains a critical challenge. The fundamental bottleneck is the difficulty of reliably integrating real-world execution outcomes into the digital decision-making loop. To address this, we propose an endogenous contribution evaluation framework integrating Decentralized Physical Infrastructure Networks (DePIN) and Vision-Language-Action (VLA) models. This approach maps physical entities to on-chain decentralized identities. By leveraging VLA edge nodes to analyze multimodal behavioral data collected via DePIN, the system autonomously generates a verifiable Proof of Real-World Contribution (PoRWC). This proof subsequently drives on-chain incentive distribution through a reputation-weighted consensus mechanism. Consequently, this framework establishes an endogenously trustworthy closed loop from physical processes to digital governance. We demonstrate its feasibility and scalability through a case study of the Chinese Association of Automation (CAA), providing a robust engineering path for the parallel governance of modern academic societies.
Federated Learning (FL) enables distributed model training while preserving data privacy; however, it remains vulnerable to poisoning attacks and lacks computational integrity. Recent solutions integrating Zero-Knowledge Proofs (ZKPs) and blockchain have successfully established process-level verifiability but suffer from prohibitive computational overhead due to the requirement of generating cryptographic proofs for every local update. To address this efficiency bottleneck, this paper proposes Pro-ZkFL, a reputation-aware probabilistic verification framework. Unlike deterministic approaches that verify every transaction, Pro-ZkFL utilizes Verifiable Random Functions (VRF) on-chain to dynamically select a subset of clients for auditing based on their historical reputation scores. We design a dual-commitment scheme where clients submit lightweight cryptographic commitments for every round but generate heavy ZKPs only when challenged. Experimental results on FashionMNIST and CIFAR-10 demonstrate that Pro-ZkFL reduces computational overhead by approximately 82 % and gas costs by 73 % compared to full-verification baselines while maintaining a 99 % detection rate against persistent adversaries, offering a scalable trade-off between security and efficiency.
Although Large Language Models (LLM) have shown impressive performance across various domains, there is a shortage of benchmarks for systematically evaluating their in-depth understanding of specialized fields such as blockchain. This study extends the Self-Instruct methodology to introduce BLADE (Blockchain Large Language model Assessment Dataset for Evaluation), a comprehensive benchmark dataset for assessing LLM comprehension in the blockchain domain. BLADE consists of a total of 1,382 questions organized according to a systematic classification of blockchain knowledge, featuring a detailed structure with 15 main categories and 5 sub-categories for each. The benchmark covers the entire spectrum of blockchain knowledge, from its fundamentals to consensus mechanisms, architecture, smart contracts, token economy, Decentralized Finance (DeFi), NFT(Non-Fungible Token)s and digital assets, security, governance, and real-world application cases. In this research, we present a benchmark generation methodology utilizing the domain knowledge of GPT-4.5, which allowed us to create high-quality evaluation items of varying difficulty and types from expert-verified seed questions. The evaluation results of various open-source LLMs, including Qwen, DeepSeek, and Kanana, on BLADE showed that current models exhibit significant differences in their understanding of blockchain, with Qwen2.5-7B-Instruct-1M achieving the highest performance. The BLADE benchmark provides a tool for precisely evaluating and improving the blockchain comprehension of LLMs, thereby promoting the effective fusion of AI and blockchain technology and contributing to the development of more reliable decentralized systems.
Money laundering in Bitcoin networks threatens social stability and undermines global economic security, making its detection a top priority for governments and relevant sectors. However, existing detection methods often struggle to extract critical transaction information due to the inherent heterogeneity and massive noise in Bitcoin networks, resulting in low detection performance. To address this challenge, we propose a novel suspicious subgraph-enhanced method to identify covert money laundering activities in Bitcoin networks. Our core innovation is a semantic-driven subgraph analysis strategy, realized as a proactive financial purification mechanism. This strategy fundamentally reframes the problem by focusing on semantic relevance over mere topology, thereby achieving active denoising and functional isolation. Crucially, it successfully mitigates the inherent feature dilution problem in Graph Neural Networks (GNNs) by providing a semantically pure, signal-rich subgraph input. Experiments on large-scale Bitcoin transaction data confirm the practical superiority of our approach, demonstrating a significant 20% increase in recall compared to traditional Graph Convolutional Network (GCN) models, with high overall accuracy (96%) and recall (98%), thus meeting stringent regulatory requirements.
Ethereum's swift development has elevated phishing scams to primary security concerns within blockchain networks. Current detection methods face three key challenges: insufficient hierarchical temporal modeling, inadequate pattern-aware structural recognition, and the lack of effective mechanisms to integrate multi-modal information. This paper presents an innovative approach for phishing detection using Multi-modal Hierarchical Temporal Graph Representation (MHTGR). Our method analyzes phishing behaviors by jointly considering temporal dynamics and structural topology of transaction data. First, we construct Hierarchical Transaction Graph Network (HTGN) to organize raw transaction records into structured graph representations. Then, multiple feature modalities are extracted through a Parallel Feature Extraction (PFE) module. Finally, these features are integrated via a Multi-modal Fusion (MMF) module for comprehensive phishing detection. Empirical evaluations conducted across multiple datasets from Ethereum demonstrate that the proposed method outperforms existing methods, providing effective solutions towards blockchain security.
The need for strong cybersecurity frameworks in the healthcare industry has increased due to the Internet of Medical Things (IoMT) devices and electronic health records (EHRs) exponential growth. This paper presents HealthSentinel-ZKP, a novel framework that leverages federated neuro-symbolic deep learning and zero-knowledge blockchain to secure electronic health data. In contrast to earlier models, HealthSentinel-ZKP combines CNNs, Transformers, and symbolic reasoning for multi-perspective intrusion detection. Federated learning and zero-knowledge proof mechanisms protect data privacy. Using an immutable ZKPenhanced blockchain, the system guarantees GDPR-compliant auditing and has a dual-stream anomaly detection architecture. HealthSentinel-ZKP is a next-generation healthcare cybersecurity paradigm, as demonstrated by experimental results on benchmark datasets that demonstrate superior performance in zero-day attack detection and privacy preservation.