This study discusses the behavior of decentralized decision-making of investment in Web3 environment, and the primary factors affecting the decision of investors, including governance with transparence and fair process, opinion of the community, fluctuations of markets, and trends of social networks. From DeFi platforms and markets of NFT, this study finds the inclination of investors towards governance with transparence and fair process when selecting projects, and decisive impacts of opinion of the community on decision. This study also finds significant impacts of social network and fluctuations of markets on short-term investment, and greater risk appetite of investors under more fluctuations of markets. This study verifies the impacts of these factors on the Web3 environment of investment with data simulation under a virtual environment, provides in-depth understanding of behavior of investment under decentralized finance and markets of NFT, and provides valuable references for related projects' design and operation.
Benjamin Appiah, Daniel Commey, Winful Bagyl-Bac, Laurene Adjei · 5 authors
Maximal Extractable Value (MEV) presents a significant challenge to the fairness and efficiency of decentralized finance (DeFi). This paper provides a game-theoretic analysis of the strategic interactions within the MEV supply chain, involving searchers, builders, and validators. A three-stage game of incomplete information is developed to model these interactions. The analysis derives the Perfect Bayesian Nash Equilibria for primary MEV attack vectors, such as sandwich attacks, and formally characterizes attacker behavior. The research demonstrates that the competitive dynamics of the current MEV market are best described as Bertrand-style competition, which compels rational actors to engage in aggressive extraction that reduces overall system welfare in a prisoner’s dilemma-like outcome. To address these issues, the paper proposes and evaluates mechanism design solutions, including commit–reveal schemes and threshold encryption. The potential of these solutions to mitigate harmful MEV is quantified. Theoretical models are validated against on-chain data from the Ethereum blockchain, showing a close alignment between theoretical predictions and empirically observed market behavior.
Decentralized data-feed systems enable blockchain-based smart contracts to access off-chain information by aggregating values from multiple oracles. To improve accuracy, these systems typically use an aggregation function, such as majority voting, to consolidate the inputs they receive from oracles and make a decision. Depending on the final decision and the values reported by the oracles, the participating oracles are compensated through shared rewards. However, such incentive mechanisms are vulnerable to mirroring attacks, where a single user controls multiple oracles to bias the decision of the aggregation function and maximize rewards. This paper analyzes the impact of mirroring attacks on the reliability and dependability of majority voting-based data-feed systems. We demonstrate how existing incentive mechanisms can unintentionally encourage rational users to implement such attacks. To address this, we propose a new incentive mechanism that discourages Sybil behavior. We prove that the proposed mechanism leads to a Nash Equilibrium in which each user operates only one oracle. Finally, we discuss the practical implementation of the proposed incentive mechanism and provide numerical examples to demonstrate its effectiveness.
Regan Meloche, Durga Sivakumar, Amal A. Anda, Sofana Alfuhaid · 7 authors
Monitoring the compliance of contract performance against legal obligations is important in order to detect violations, ideally, as soon as they occur. Such monitoring can nowadays be achieved through the use of smart contracts, which provide protection against tampering as well as some level of automation in handling violations. However, there exists a large gap between natural language contracts and smart contract implementations. This paper introduces a Web-based environment that partly fills that gap by supporting the user-assisted refinement of Symboleo specifications corresponding to legal contract templates, followed by the automated generation of monitoring smart contracts deployable on the Hyperledger Fabric platform. This environment, illustrated using a sample contract from the transactive energy domain, shows much potential in accelerating the development of smart contracts in a legal compliance context.
The paper presents an opposing rule-based signed Friedkin-Johnsen (SFJ) model for the evolution of opinions in arbitrary network topologies with signed interactions and stubborn agents. The primary objective of the paper is to analyse the emergent behaviours of the agents under the proposed rule and to identify the key agents which contribute to the final opinions, characterised as influential agents. We start by presenting some convergence results which show how the opinions of the agents evolve for a signed network with any arbitrary topology. Throughout the paper, we classify the agents as opinion leaders (sinks in the associated condensation graph) and followers (the rest). In general, it has been shown in the literature that opinion leaders and stubborn agents drive the opinions of the group. However, the addition of signed interactions reveals interesting behaviours wherein opinion leaders can now become non-influential or less influential. Further, while the stubborn agents always continue to remain influential, they might become less influential owing to signed interactions. Additionally, the signed interactions can drive the opinions of the agents outside of the convex hull of their initial opinions. Thereafter, we propose the absolute influence centrality measure, which allows us to quantify the overall influence of all the agents in the network and also identify the most influential agents. Unlike most of the existing measures, it is applicable to any network topology and considers the effect of both stubbornness and signed interactions. Finally, simulations are presented for the Bitcoin Alpha dataset to elaborate the proposed results.
Chi‐Wei Su, Yu‐Mei Ding, Kai‐Hua Wang, Xiaoqing Wang
ABSTRACT In this paper, the safe‐haven attributes of green bonds, gold, and bitcoin are compared to those of traditional bonds under various time periods and quantiles by using the WQC methodology. The results indicate that green bonds exhibited a stable safe‐haven function at longer time horizons during the full sample period, whereas other assets did not have safe‐haven features. During the COVID‐19 pandemic, bitcoin exhibited safe‐haven attributes at all time horizons, whereas gold demonstrated these characteristics over the short and medium terms. In the sample period during the Russia–Ukraine war, green bonds had strong safe‐haven properties at shorter and middle time horizons, whereas bitcoin had these properties at longer time spans. In this paper, a multivariate network framework that includes green bonds, gold, and bitcoin is constructed, and the theoretical foundations that influence the safe‐haven attributes of assets are detailed. In addition, this study clearly presents the safe‐haven effects of assets under various sample periods, time horizons, and quantiles, thereby bridging the gap of existing studies that ignore time frequency. Thus, this paper provides advice for investors, regulators, and policy‐makers, such as choosing portfolios on the basis of asset characteristics, monitoring asset disclosure, and encouraging the trading of safe‐haven assets.
Aim . To reveal the ideological nature of digital decentralization as a systemic challenge to traditional state sovereignty and to identify risks for modern states amid technological transformation. Methodology . The core of the study comprises an analysis of key digital decentralization ideologies (crypto-anarchism, cyber-syndicalism, cypherpunk), their technological foundations, and implementation practices. A comparative analysis of foundational manifestos by crypto-anarchists and cypherpunks (T. May, E. Hughes) was conducted, and the evolution of decentralized movements was synthesized. Results . The analysis demonstrated that the synergy of technologies and extra-systemic ideologies creates parallel governance systems undermining the state’s monopoly on regulating finance, information, law, and the exercise of power. Threats to modern states include: erosion of trust in institutions, use of decentralized digital resources for protest mobilization, sanctions evasion via cryptocurrencies, and increased citizen registrations in virtual jurisdictions operating beyond national law. Research implications . Proposals for state adaptation are formulated: shifting from technology bans to dialogue with IT communities and developing preventive measures. The author introduces an original interpretation of digital decentralization as “engineering autocracy”, where algorithmic power replaces political-legal mechanisms. The study reframes issues of state sovereignty in the context of competition with decentralized anti-systems.
This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and Decentralized Finance (DeFi) trading strategies through extensive backtesting on 10 models across multiple cryptocurrency assets. Our analysis encompasses classical ML models (Random Forest, Gradient Boosting, Logistic Regression), pure quantum models (VQE Classifier, QNN, QSVM), hybrid quantum-classical models (QASA Hybrid, QASA Sequence, QuantumRWKV), and transformer models. The results demonstrate that hybrid quantum models achieve superior overall performance with 11.2\% average return and 1.42 average Sharpe ratio, while classical ML models show 9.8\% average return and 1.47 average Sharpe ratio. The QASA Sequence hybrid model achieves the highest individual return of 13.99\% with the best Sharpe ratio of 1.76, demonstrating the potential of quantum-classical hybrid approaches in AMM and DeFi trading strategies.
Blockchain technology offers decentralization and security but struggles with scalability, particularly in enterprise settings where efficiency and controlled access are paramount. Sharding is a promising solution for private blockchains, yet existing approaches face challenges in coordinating shards, ensuring fault tolerance with limited nodes, and minimizing the high overhead of consensus mechanisms like PBFT. This paper proposes the Range-Based Sharding (RBS) Protocol, a novel sharding mechanism tailored for enterprise blockchains, implemented on Quorum. Unlike traditional sharding models such as OmniLedger and non-sharding Corda framework, RBS employs a commit-reveal scheme for secure and unbiased shard allocation, ensuring fair validator distribution while reducing cross-shard transaction delays. Our approach enhances scalability by balancing computational loads across shards, reducing consensus overhead, and improving parallel transaction execution. Experimental evaluations demonstrate that RBS achieves significantly higher throughput and lower latency compared to existing enterprise sharding frameworks, making it a viable and efficient solution for largescale blockchain deployments.
The Open Network (TON) is a high-performance blockchain platform designed for scalability and efficiency, leveraging an asynchronous execution model and a multi-layered architecture. While TON's design offers significant advantages, it also introduces unique challenges for smart contract development and security. This paper introduces a comprehensive audit checklist for TON smart contracts, based on an empirical analysis of 34 professional audit reports containing 233 real-world vulnerabilities. The checklist addresses TON-specific challenges, such as asynchronous message handling, and provides actionable insights for developers and auditors. We also present detailed case studies of vulnerabilities in TON smart contracts, highlighting their implications and offering lessons learned. To validate practical utility, we conducted a practitioner survey (n=11 complete responses), confirming the checklist's value alongside automated tools. By adopting this checklist, developers and auditors can systematically identify and mitigate vulnerabilities, enhancing the security and reliability of TON-based projects. Our work bridges the gap between Ethereum's mature audit methodologies and the emerging needs of the TON ecosystem, fostering a more secure and robust blockchain environment.
Tržište kriptovaluta jedno je od najmlađih financijskih tržišta, koje se razvija usporedno s napretkom digitalnih tehnologija. Osnovna obilježja ovoga tržišta proizlaze iz njegove nereguliranosti i izražene volatilnosti cijena, pri čemu se posebno ističe značaj malih investitora i pojedinaca kao aktivnih sudionika. Cilj ovoga rada jest istražiti utjecaj različitih bihevioralnih faktora na investicijske odluke i namjere ulaganja u kriptovalute. U teorijskom dijelu prikazana su temeljna obilježja kriptovaluta i financijskih tržišta, kao i ključni koncepti bihevioralne ekonomije. Poseban naglasak stavljen je na kognitivne pristranosti poput pretjeranog samopouzdanja, efekta FOMO, averzije prema gubitku i žaljenju, utjecaja influencera, gamifikacije te zablude kockara. Empirijski dio rada temelji se na anketnom istraživanju u kojem je sudjelovalo 208 ispitanika, a prikupljeni podaci analizirani su pomoću metoda deskriptivne statistike i višestruke regresije. Rezultati pokazuju da su pojedini bihevioralni faktori, prije svega FOMO, gamifikacija i kockarska zabluda, statistički značajni u objašnjavanju investicijske namjere. Takvi nalazi potvrđuju teorijske pretpostavke bihevioralnih financija te pokazuju da emocionalni i kognitivni obrasci značajno oblikuju odluke investitora na tržištu kriptovaluta. Zaključno, istraživanje doprinosi boljem razumijevanju ponašanja ulagača u kontekstu kriptovaluta i ističe potrebu za daljnjim proučavanjem utjecaja psiholoških čimbenika na financijsko odlučivanje. Dobiveni rezultati ujedno pokazuju kako tradicionalna financijska teorija, koja pretpostavlja racionalnost investitora, nije dostatna za objašnjenje investicijskog ponašanja na suvremenim tržištima.
Globalized supply chains are strained by fragmented data, multi-tier opacity, counterfeit risks, and costly disputes. Blockchain—a shared, append-only ledger—has been proposed to enhance transparency, traceability, and operational efficiency, yet real-world adoption reveals both breakthroughs and bottlenecks. This paper develops a deploymentminded view that integrates GS1 EPCIS/CBV standards for interoperable event data, permissioned ledgers for governance, and privacy-preserving proofs (zero-knowledge) to reconcile transparency with business confidentiality. We synthesize evidence from systematic reviews and flagship pilots (e.g., Walmart–IBM Food Trust) and contrast them with lessons from initiatives that wound down (e.g., TradeLens), extracting adoption patterns, KPI impacts, and failure modes. We then describe a reference methodology—data acquisition via EPCIS events, Fabric-based channels, and role-based access—plus an evaluation rubric for trace time, recall precision, dispute cycle time, and data-reconciliation costs. Results from literature-anchored benchmarks indicate orders-of-magnitude traceability lead-time (TLT) reductions (days → seconds) and measurable reductions in manual reconciliation, with gains contingent on standards compliance and high-quality “oracle” data. Finally, we map future directions—zk-proof rollups, interoperable digital product passports, and policy-aligned sustainability metrics—alongside candid limitations around ecosystem incentives, privacy, scalability, and data veracity. We conclude that blockchain can shift chains from reactive to verifiable and auditable networks when combined with data standards, sound governance, and selective privacy technologies rather than “full transparency” alone.
K. Nirmala Devi, Lakshmi Narasimha, N. Sujatha, Y. Geetha · 6 authors
The integration of blockchain technology into financial markets has sparked significant scholarly interest, particularly in the context of stock market prediction. This bibliometric analysis aims to provide a comprehensive overview of research trends, influential publications, and emerging themes within this interdisciplinary domain from 2018 to 2025. Drawing data from Scopus the study utilizes bibliometric tools such as Biblioshiny and VOSviewer to analyse publication outputs, citation patterns, co-authorship networks, and keyword co-occurrence. The findings reveal a consistent growth in academic contributions, especially after 2019, reflecting blockchain’s increasing relevance in financial prediction and its convergence with machine learning, deep learning, and artificial intelligence. Key research clusters identified include algorithmic trading, decentralized finance (DeFi), cryptographic modelling, and predictive analytics. The analysis also highlights leading journals, authors, and institutions contributing to the advancement of this field. However, certain limitations are acknowledged. The focus on selected databases may have excluded valuable contributions from platforms such as IEEE Xplore, SSRN, or non-indexed proceedings. Additionally, the keyword-based search strategy may have overlooked studies using alternative terminologies. The temporal scope may also bias the analysis toward recent developments while underrepresenting foundational research. The study offers a valuable reference point for scholars and practitioners, mapping the intellectual structure and thematic progression of blockchain-based stock prediction research. Future studies are encouraged to adopt multi-database approaches, combine quantitative and qualitative methods, and explore regulatory and regional variations to enrich understanding and guide practical implementation.
Integration of Federated Learning (FL) with Blockchain technology to decentralized privacy-preserving, and scalable framework for strengthening cybersecurity. As cyber threats like ransomware, malware, and network intrusions grow in complexity, there is an increasing need for collaborative threat detection and mitigation. However, traditional collaborative approaches often involve sharing sensitive information across organizations, raising significant privacy concerns and regulatory challenges under frameworks like GDPR and HIPAA. FL works to solve these problems through enabling multiple entities to work together on training machine learning models without sharing their original information. Despite its advantages, FL faces challenges such as the risk of model tampering, trust deficits between participants, and dependence on a centralized server for model aggregation. To overcome these limitations the Blockchain technologies will be in used so blockchain technology provides a distributed, transparent, and non-mutable ledger that safely manages FL operations. It helps preserve the accuracy and trustworthiness of model updates via smart contracts along with consensus mechanisms, bypassing the requirement fora central aggregator. In addition, blockchain enables incentivization by introducing token-based rewards, encouraging active participation in collaborative threat detection networks. Privacy- preserving techniques to boost information security, techniques like differential privacy and homomorphic encryption are also put into practice. Such a integration of FL and blockchain is particularly impactful in securing distributed systems such as IoT devices, critical infrastructure, and enterprise networks, where privacy, trust, and scalability are crucial. This project aims to demonstrate the practical implementation of this framework, paving the way for adaptive and globally scalable cyber security systems to combat evolving threats.
The rapid advancement of artificial intelligence (AI) and large language models (LLMs) is profoundly reshaping higher education, shifting from institution-centered paradigms to learner-centric personalized learning environments (PLEs). However, PLEs face critical challenges in identity management, including data breaches, unauthorized access, and interoperability barriers, which undermine security and trust. This study proposes the Blockchain-based Student Identity Management System (BSIMS), a conceptual model integrating blockchain technology, xAPI standards, and OAuth2 protocols to uphold confidentiality, integrity, availability, authenticity, and non-repudiation (CIAAN) principles. Grounded in the Technology Acceptance Model (TAM) and Information Systems Success Model (ISSM), BSIMS was validated through mixed-methods research involving 90 students and 16 experts from five Yunnan Province universities. Results demonstrate BSIMS' superiority in user satisfaction (explaining 81.7% variance), CIAAN performance (M=4.58 vs. 3.18 for traditional systems, p<0.001), and reliability (zero downtime, 0.3 ms response time). Ethical and legal implications, such as immutability conflicts with privacy rights, are addressed via zero-knowledge proofs and off-chain storage. BSIMS offers a scalable framework for secure PLEs, advancing educational informatization in Yunnan and beyond.
Abstract By converting between currencies, cryptocurrency exchanges provide access between the traditional and cryptocurrency ecosystem, making them susceptible to money laundering. The European Union extended the scope of the 5 $$^{\text {th}}$$ Anti-Money Laundering Directive (AMLD5) to include cryptocurrency exchanges, requiring them to obtain a registration, conduct customer due diligence, and report unusual transactions. It is, however, unknown whether the measures introduced by the implementation of AMLD5 lead to less risk exposure and what impact it has on cryptocurrency exchanges. This paper uses a mixed-methods approach to explore the effects of the Dutch implementation of AMLD5 measures on cryptocurrency exchanges active in the Netherlands. We analyzed over 335,000 transactions and complemented them with seven qualitative interviews with Dutch cryptocurrency exchanges and the supervisory authority. We find that the Dutch implementation of AMLD5 imposed high administrative burdens and substantial fees on relatively small exchanges that do not pose high money laundering risks. This raises questions about the alignment of the goals and consequences of the regulation.
Wan Amir Azlan Wan Haniff, Redwan Yasin, Rahmawati Mohd Yusoff, Asma Hakimah Ab Halim · 6 authors
The article investigates the challenges and prospects of the ruling of Waqf Crowdfunding (Waqf-CF) scheme adoption in Malaysia as Shariah-compliant fintech successors deployed to mobilize Islamic endowment. However, the implementation of Waqf-CF is hindered by a number of challenges, such as the uncertainty of the legal aspects and fragmented governance, along with technology limitations and Shariah compliance issues. Using a qualitative approach, insights were gathered from seven experts 7 experts in finance, academia, and business to inform and guide our work. The results suggest that poor coordination of regulation between federal and state governments, varied modes of governance, and a lack of fintech literacy in waqf bodies are the barriers to successful implementation. In this regard, the paper examines the Waqf-CF models currently being used, including the Crowdfunding-Waqf Model and the Hasanah Platform, by highlighting the pros and cons of each. Based on these, the authors present a sophisticated hybrid model combining blockchain-based smart contracts, AI-led risk profiling, and real-time Shariah auditing for increased trust, transparency, and scalability. Finally, the paper calls for the need of a national regulatory framework and better institutional support to drive Waqf Crowdfunding as an ethical and sustainable funding option that is in line with Maqasid al-Shariah and the nation’s vision to be a global Islamic financial hub.
Christoph Hochrainer, Valentin Wüstholz, Maria Christakis
Zero-knowledge virtual machines (zkVMs) are increasingly deployed in decentralized applications and blockchain rollups since they enable verifiable off-chain computation. These VMs execute general-purpose programs, frequently written in Rust, and produce succinct cryptographic proofs. However, zkVMs are complex, and bugs in their constraint systems or execution logic can cause critical soundness (accepting invalid executions) or completeness (rejecting valid ones) issues. We present Arguzz, the first automated tool for testing zkVMs for soundness and completeness bugs. To detect such bugs, Arguzz combines a novel variant of metamorphic testing with fault injection. In particular, it generates semantically equivalent program pairs, merges them into a single Rust program with a known output, and runs it inside a zkVM. By injecting faults into the VM, Arguzz mimics malicious or buggy provers to uncover overly weak constraints. We used Arguzz to test six real-world zkVMs (RISC Zero, Nexus, Jolt, SP1, OpenVM, and Pico) and found eleven bugs in three of them. One RISC Zero bug resulted in a $50,000 bounty, despite prior audits, demonstrating the critical need for systematic testing of zkVMs.
ABSTRACT Despite the universal acknowledgment of financial profit expectations as an investment driver, environmental concern has been suggested as a factor influencing investors' decisions to purchase cryptocurrency. In this sense, this study investigates the impact of environmental information on investment allocation decisions to purchase different types of cryptocurrencies with different levels of environmental impacts (i.e., cryptocurrencies using Proof‐of‐Work (Bitcoin) and Proof‐of‐Stake (Ether) consensus algorithms). This study used an online survey involving 199 respondents in experimental groups (receiving environmental information before allocating decision) and control groups (receiving no environmental information before allocating decision) to split an imaginary fund into Bitcoin and Ether. No significant difference in allocating capital was found between the groups regardless of investment horizon, time of affiliation as a cryptocurrency investor, education level of the respondents, and perceived importance of environmental impacts. Possible explanations for this insensitivity are widespread prior knowledge about the environmental impact of Bitcoin, psychological reactance towards environmental information, and the assessed overall low perceived importance of environmental impact for investment decisions in cryptocurrencies. The lack of significant impact found in such an experimental study implies that environmental education alone cannot be sufficient to shift investor preferences. The findings offer initial insights into the impact of environmental awareness on cryptocurrency investment motivations and provide empirical evidence to understand cryptocurrency investment behaviors in the current research scene. The results suggest researchers and policymakers investigate further investors' motives while coming up with more restrictive policy instruments to mitigate the negative environmental impact of cryptocurrencies.
The smart grid is the next evolution of electrical power systems, a continuation of the old grids that involves a mix of digital and traditional power grid technologies to allow the potential to communicate in both directions, decentralized energy production and real-time monitoring. However, such a connection exposes it to cyber attacks, data fraud, and unauthorized access as well. Blockchain technology is one of these technologies because it is transparent, immutable, and decentralized to overcome these security obstacles. In this paper, an overview of blockchain technology smart grid security, architecture, consensus algorithm, and application are presented. Some of the most notable blockchain works in the smart grid include secure energy trading, decentralised identity management, detecting attacks and preserving privacy. When applied in smart grids, reviewed blockchain protocols also comprise Proof of Work (PoW) and Proof of Stake (PoS) along with Practical Byzantine Fault Tolerance (PBFT). Top of that, there are hybrid types of blockchain such as artificial intelligence (AI) and the Internet of things (IoT) that are also covered as the next picture to enable the system to become more scalable and interoperable. Power consumption, time wastage, and regulation hurdle is greatly considered. This paper has concluded that blockchain is a bottom-up technology, which can cause smart grid infrastructures to be much more resilient, transparent, and efficient.
Abstract This paper presents a systematic literature review of 137 peer-reviewed publications from 41 journals, examining the interconnectedness between cryptocurrencies and traditional financial markets. Using a rigorous three-stage methodology for study selection, we identify key research themes including spillover effects, volatility transmission, interdependence, hedge effectiveness, and safe-haven properties of cryptocurrencies. Our analysis reveals that GARCH-based models dominate early work on volatility and contagion, while more recent studies adopt advanced approaches, such as cross-quantilogram, wavelet coherence, and multifractal detrended cross-correlation, to capture non-linear, time-varying relationships without assuming stationarity. Our review offers three major contributions. First, we provide a comprehensive classification of the interconnectedness between different types of cryptocurrencies and financial markets, highlighting their evolving roles as hedges, safe havens, or diversifiers. Second, we synthesize empirical findings to show how spillovers, time-varying correlations, tail dependencies, and contagion risks intensify under major events, such as COVID-19, regulatory shifts, and geopolitical conflicts. Third, we draw attention to overlooked areas, including emerging market dynamics and macroeconomic determinants. We recommend that policymakers implement early warning systems and proactively monitor volatility and connectedness in crypto markets to reduce contagion risks and maintain financial stability. Policy frameworks should consider the unique features of crypto markets and the time-varying interlinkages between cryptos, commodities, fiat currencies, and equities. Investors, in turn, should track cryptocurrency price movements closely, as they provide valuable signals for forecasting broader market trends and improving portfolio risk management. These insights have practical implications for risk mitigation and decision-making in increasingly integrated financial systems.
Mohammed Ziaul Haider, Tayyaba Noreen, Mishah Uzziél Salman, Marcos Dias de Assunção · 5 authors
Cross-chain bridges and oracle DAOs represent some of the most vulnerable components of decentralized systems, with more than 2.8 billion lost due to trust failures, opaque validation behavior, and weak incentives. Current oracle designs are based on multisigs, optimistic assumptions, or centralized aggregation, exposing them to attacks and delays. Moreover, predictable committee selection enables manipulation, which threatens data integrity across chains. We propose V-ZOR, a verifiable oracle relay that integrates zero-knowledge proofs, quantum-grade randomness, and cross-chain restaking to mitigate these risks. Each oracle packet includes a Halo 2 proof verifying that the reported data was correctly aggregated using a deterministic median. To prevent committee manipulation, VZOR reseeds its VRF using auditable quantum entropy, ensuring unpredictable and secure selection of reporters. Reporters stake once on a shared restaking hub; any connected chain can submit a fraud proof to trigger slashing, removing the need for multisigs or optimistic assumptions. A prototype in Sepolia and Scroll achieves sub-300k gas verification, one-block latency, and a $\mathbf{1 0} \times$ increase in collusion cost. V-ZOR demonstrates that combining ZK attestation with quantum-randomized restaking enables a trust-minimized, high-performance oracle layer for cross-chain DeFi.
In disaster scenarios where conventional energy infrastructure is compromised, secure and traceable energy trading between solar-powered households and mobile charging units becomes a necessity. To ensure the integrity of such transactions over a blockchain network, robust and unpredictable nonce generation is vital. This study proposes an SDN-enabled architecture where machine learning regressors are leveraged not for their accuracy, but for their potential to generate randomized values suitable as nonce candidates. Therefore, it is newly called Proof of AutoML. Here, SDN allows flexible control over data flows and energy routing policies even in fragmented or degraded networks, ensuring adaptive response during emergencies. Using a 9000-sample dataset, we evaluate five AutoML-selected regression models - Gradient Boosting, LightGBM, Random Forest, Extra Trees, and K-Nearest Neighbors - not by their prediction accuracy, but by their ability to produce diverse and non-deterministic outputs across shuffled data inputs. Randomness analysis reveals that Random Forest and Extra Trees regressors exhibit complete dependency on randomness, whereas Gradient Boosting, K-Nearest Neighbors and LightGBM show strong but slightly lower randomness scores (97.6%, 98.8% and 99.9%, respectively). These findings highlight that certain machine learning models, particularly tree-based ensembles, may serve as effective and lightweight nonce generators within blockchain-secured, SDN-based energy trading infrastructures resilient to disaster conditions.
Ye Tian, Yifan Jia, Yanbin Wang, Jianguo Sun · 7 authors
The success of smart contracts has made them a target for attacks, but their closed-source nature often forces vulnerability detection to work on bytecode, which is inherently more challenging than source-code-based analysis. While recent studies try to align source and bytecode embeddings during training to transfer knowledge, current methods rely on graph-level alignment that obscures fine-grained structural and semantic correlations between the two modalities. Moreover, the absence of precise vulnerability patterns and granular annotations in bytecode leads to depriving the model of crucial supervisory signals for learning discriminant features. We propose ExDoS to transfer rich semantic knowledge from source code to bytecode, effectively supplementing the source code prior in practical settings. Specifically, we construct semantic graphs from source code and control-flow graphs from bytecode. To address obscured local signals in graph-level contract embeddings, we propose a Dual-Attention Graph Network introducing a novel node attention aggregation module to enhance local pattern capture in graph embeddings. Furthermore, by summarizing existing source-code vulnerability patterns and designing corresponding bytecode-level patterns for the three target vulnerabilities, we provide an aligned pattern framework that facilitates fine-grained cross-modal alignment and the capture of function-level vulnerability signals. Finally, we propose a dual-focus objective for our cross-modal distillation framework, comprising: a Global Semantic Distillation Loss for transferring graph-level knowledge and a Local Semantic Distillation Loss enabling expert-guided, fine-grained vulnerability-specific distillation. Experiments on real-world contracts demonstrate that our method achieves consistent F1-score improvements (3%--6%) over strong baselines.