Code reuse is a common practice in software engineering. Developers of smart contracts pervasively reuse subcontracts to improve development efficiency. Like any program language, such subcontract reuse may unexpectedly include, or introduce vulnerabilities to the end-point smart contract. Indeed, prior empirical studies have identified a number of issues caused by code reuse in smart contracts. Unfortunately, automatically detecting such issues poses several unique challenges. Particularly, in most cases, smart contracts are compiled as bytecode, whose class-level information (e.g., inheritance, virtual function table), and even semantics (e.g., control flow and data flow) are fully obscured as a single smart contract after compilation. Therefore, it is rather difficult to identify the reused parts of subcontract from a given smart contract, not to mention finding potential vulnerabilities caused by subcontract misuse.In this paper, we propose Satellite, a new bytecode-level static analysis framework for subcontract misuse vulnerability (SMV) detection in smart contracts. Satellite incorporates a series of novel designs to enhance its overall effectiveness.. Particularly, Satellite utilizes a transfer learning method to recover the inherited methods, which are critical for identifying subcontract reuse in smart contracts. Further, Satellite extracts a set of fine-grained method-level features and performs a method-level comparison, for identifying the reuse part of subcontract in smart contracts. Finally, Satellite summarizes a set of SMV indicators according to their types, and hence effectively identifies SMVs. To evaluate Satellite, we construct a dataset consisting of 58 SMVs derived from real-world attacks and collect additional 56 SMV patterns from SOTA studies. Experiment results indicate that Satellite exhibits good performance in identifying SMV, with a precision rate of 84.68% and a recall rate of 92.11%. In addition, Satellite successfully identifies 14 new/unknown SMV over 10,011 realworld smart contracts, affecting a total amount of digital assets worth 201,358 USD.
Abstract Health Care Information Systems leverage Body Area Networks (BANs) to provide real-time monitoring and automated medical interventions, significantly enhancing patient care. However, security and privacy concerns present significant barriers to widespread adoption, with broken access control being a considerable risk. This research proposes an authorization framework to secure BANs, addressing critical issues such as unauthorized access and policy enforcement failures in electronic health records (EHRs). Our study introduces a Multi-Modular System Architecture that enhances access control, incorporating a Spatio-Temporal Attribute-Based Access Control (STABAC) model to enforce dynamic location and time constraints for secure data access. We introduce the Spatio-Temporal Zone (STZone) concept, simplifying policy enforcement by integrating time and location attributes. To ensure policy integrity and security, we employ Time Colored Petri Nets (TCPN) for formal policy analysis, detecting violations, and ensuring compliance with real-time constraints. Additionally, blockchain technology is leveraged to maintain policy integrity, preventing unauthorized modifications. Experimental validation demonstrates the effectiveness of the proposed framework in enforcing secure access control while maintaining system usability. The findings highlight the framework’s potential in securing BANs, offering a scalable and adaptable approach to mitigating emerging security threats in healthcare information systems.
With the rapid advancement of blockchain technology and the expanding influence of decentralized finance (DeFi), centralized trading systems (CEX) increasingly confront critical challenges including single points of failure, insufficient data transparency, and elevated transaction costs. Therefore, it is imperative to develop and explore decentralized trading systems that are compatible with and adaptive to quantitative trading strategies. This study focuses on constructing a framework for decentralized trading systems suitable for quantitative strategies, aiming to reduce transaction costs, enhance strategy transparency, and improve risk resistance capabilities. By analyzing the technical architecture (including public chain adaptations: Solana for high-frequency strategies and Ethereum Layer 2 for medium-frequency strategies), innovations in decentralized exchanges (DEX) (such as order-book based dYdX and AMM based Uniswap V3), and the application of on-chain quantitative tools (like Nansen for whale behavior tracking and automated execution tools based on Aave smart contracts), the study confirms the feasibility and advantages of decentralized design in quantitative scenarios.
Abstract The current academic landscape is often shaped by political and financial influences, restricting access to education and compromising research independence. To address these challenges, we introduce “FreeUniversity.dao”, a university model grounded in the principles of a Decentralized Autonomous Organization (DAO). FreeUniversity.dao emphasizes academic freedom, openness, and equitable access to education, leveraging blockchain and DeFi technologies to establish a transparent and sustainable framework for global learning and research. It envisions a politically neutral and corruption-free governance model that prioritizes transparency, fairness, and accessibility. Decision-making processes are designed to be participatory, democratic, rewarding merits and contributions, and involving the community in all major decisions, including rule changes and further developments. For traditional university institutions FreeUniversity.dao offers a migration path to DAO-based structures. While this paper does not present a fully realized DAO design, it outlines the core concepts, coordination challenges, and foundational elements required to build such a system. It aims to be a starting point for further exploration and discussion about the future of academic governance. An example workflow illustrates how the DAO could work in practice and motivates how current processes can be improved to reduce interventions that could jeopardize the integrity of scientific research.
Ensuring the correctness of smart contracts is critical, as even subtle flaws can lead to severe financial losses. While bug detection tools able to spot common vulnerability patterns can serve as a first line of defense, most real-world exploits and losses stem from errors in the contract business logic. Formal verification tools such as SolCMC and the Certora Prover address this challenge, but their impact remains limited by steep learning curves and restricted specification languages. Recent works have begun to explore the use of large language models (LLMs) for security-related tasks such as vulnerability detection and test generation. Yet, a fundamental question remains open: can LLMs aid in assessing the validity of arbitrary contract-specific properties? In this paper, we provide the first systematic empirical evaluation of GPT-5, a state-of-the-art reasoning LLM, in this role. We benchmark its performance on a large dataset of verification tasks, compare its outputs against those of established formal verification tools, and assess its practical effectiveness in real-world auditing scenarios. Our study combines quantitative metrics with qualitative analysis, and shows that recent reasoning-oriented LLMs - although lacking soundness guarantees - can be surprisingly effective at predicting the (in)validity of complex properties, suggesting a new frontier in the convergence of AI and formal methods for secure smart contract development and auditing.
With the proliferation of new blockchain-based cryptocurrencies/assets and platforms that make it possible to transact across them, it becomes important to consider not just whether the transfer of coins/assets can be tracked within their respective transaction ledger, but also if they can be tracked as they move across ledgers. This is especially important given that there are documented cases of criminals attempting to use these cross-ledger trades to obscure the flow of their coins/assets. In this paper, we perform a systematic review of the various tracing techniques for blockchain transactions proposed in literature, categorize them using multiple criteria (such as tracing approach and targeted objective) and compare them. Based on the above categorization, we provide insights on the state of blockchain transaction tracing literature and identify the limitations of existing approaches. Finally, we suggest directions for future research in this area based on our analysis.
The Healthcare Revenue Cycle Management (RCM) landscape is struggling with issues such as coding complexity, payer diversity, labor shortage, and data silos. The Collaborative Core proposes a Human-in-the-Loop (HITL) Artificial Intelligence model that will be used to strategically combine AI efficiency and human judgment throughout the RCM lifecycle. This model is cost-effective as it directs mundane work to AI automation, leaving human insight to make complex decisions, which would congruently improve both efficiency and accuracy. The framework makes use of interoperability standards, distributed ledger technologies, and sophisticated AI tools, along with explicit handoff procedures between fully automated and human processes. The results of the implementation have shown positive changes in the charge capture process, claim processing, posting of payments, and the ability to manage denials, and improve provider, insurer, and patient outcomes. The Collaborative Core is an innovative solution to the complex problem of healthcare financial management, which is used in collaboration with current trends toward technological control by ensuring that it is not lost in the algorithmic bias and human adjustment to working with technology.
Abstract The President of India, in her address to Parliament in June 2009, stressed on achieving the goals of equity or inclusion, innovation and accountability in public policies and the governance process. She said that the Government is committed to creating an innovation strategy for government, industry, entrepreneurs, technologists and academicians with a focus on inclusive growth and appropriate eco-system necessary to bring about generational change in our approach to development. The Thirteenth Finance Commission emphasized the need for adopting innovative methods and focus on capacity building, decentralization and public accountability in public systems. A number of citizen centric administrative innovations are introduced by the government at center and state level. The paper elaborates on various initiatives taken by the Government of India in using innovative methods and use of technology in the process of governance to make it citizen centric and facilitating good governance.
Ubrzani razvoj blockchain tehnologije i ulazak novih kriptovaluta na tržište otvorili su mogućnost primjene kripto tehnologije u različitim industrijama, uključujući turizam i hotelijerstvo. U ovom radu analiziran je potencijal implementacije kriptovaluta u hotelskoj industriji s naglaskom na korištenje kriptovaluta kao sredstva plaćanja. Teorijski dio rada pruža pregled temeljnih značajki kriptovaluta, globalnih trendova u njihovoj upotrebi te prednosti i izazova njihove primjene u hotelijerstvu. Nadalje, analiziraju se globalni trendovi upotrebe kriptovaluta, posebno rast broja korisnika i sve veća prihvaćenost digitalne imovine u platnom prometu, ali i ograničenja kao što su volatilnost te različite regulatorne prakse koje otežavaju njihovu širu primjenu. Kroz modele prihvaćanja tehnologije objašnjava se kako percipirana korisnost, jednostavnost korištenja, tehnološka osviještenost i povjerenje u sustav utječu na namjeru korištenja kripto plaćanja. Empirijski dio temelji se na anketnom istraživanju provedenom među građanima Republike Hrvatske. Anketa je ispitala razinu poznavanja kripto tehnologije, posjedovanja kriptovaluta te namjere korištenja za plaćanje hotelskih usluga kako bi se ocijenilo što će biti potrebno za uspješnu implementaciju u hotelijerskom sektoru te mogu li se uvođenjem ove tehnologije ostvariti konkurentske prednosti.
Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali
Bitcoin market has exhibited substantial volatility over time.Bitcoin returns exhibit high standard deviation.This study employs the GARCH (1,1) model with normal (norm), Studentt (std), and generalized error distributions (ged) to estimate Bitcoin conditional volatility.Bitcoin exhibits fat-tailed returns, volatility clustering, and a remarkably high persistence value.The GARCH (1,1)-ged model showed superior performance compared to other models when evaluated using LL, AIC, and BIC criteria.The indicator saturation (IS) method was employed to concurrently detect historical daily breaks, trend breaks, and outliers in Bitcoin volatility data.The indicator saturation approach revealed that, for the past decade, historical Bitcoin volatility has had 6 outliers, 31 breaks, and 74 trend breaks under the normal distribution, 0 outliers, 26 breaks, and 83 trend breaks under the student-t distribution, and 1 outlier, 29 breaks, and 77 trend breaks under the ged distribution.This shows that assuming a heavy tail led to fewer outliers and breaks, and as the frequency of trend breaks increases, it also shows more volatility clusters represented by GARCH.These discoveries have the potential to comprehend the influence of events on financial markets and guarantee stability in the evaluation of financial risk, management of portfolios, and modeling endeavors.
This study analyzes the two-tier local government models in France, Japan, Germany, and the United States to draw lessons for reforming the organization of state apparatus in Vietnam. Based on the theoretical foundations of local government organization, it clarifies the principles of decentralization, autonomy, accountability, and effective public governance. International experience highlights the importance oftransparent institutions, financial autonomy, competent personnel, and the application of technology in governance. In Vietnam, the two-tier model has been implemented since July 1, 2025, under Law No.72/2025/QH15, replacing the traditional three-tier structure. The article examines the challenges during the transition and proposes solutions to improve the legal framework, ensure substantive decentralization, enhance implementation capacity, reform public finance, strengthen oversight, and promote accountability - thereby contributing to building an effective, modern, and democratic local government.
This study explores the application of Self-Sovereign Digital Identity (SSDI) and blockchain technology in forest supply chain management to improve traceability, sustainability and regulatory compliance. It addresses how these technologies can overcome the limitations of traditional identity management and improve forestry operations’ transparency, efficiency, and environmental accountability. An Ethereum-based blockchain framework was used for this study, focusing on metrics such as transaction throughput and latency. Experimental tests were conducted to analyze the performance of SSDI in forest supply chains, focusing on real-time data management and secure identity control. A framework aligned with the Forest 4.0 initiative was proposed to evaluate the efficacy of SSDI. The results show that the integration of SSDI with blockchain significantly improves traceability and sustainability within forest supply chains, with high transaction rates and reduced latency. The decentralized system improves transparency and trust, promotes efficient identity management among stakeholders, and improves compliance with environmental regulations. Our study is among the first to apply SSDI in forestry, advancing digital transformation in this sector. Demonstrating SSDI’s capacity to streamline data handling and boost traceability, it offers practical recommendations for stakeholders seeking sustainable and digitally secure supply chain management practices. • Improve traceability using blockchain-enabled identity systems in forest supply chains. • Enhance transparency with decentralized records and digital identity verification. • Analyze performance through transaction rates and latency metrics in real-time. • Streamline data handling with efficient credential issuance and verification. • Promote sustainability through intelligent integration of digital and monitoring tools.
ABSTRACT This paper develops a model of a cryptocurrency by incorporating mining into the otherwise standard search‐theoretic monetary framework. As usual, multiple equilibria exist. To obtain a sharp prediction on whether a cryptocurrency' s value will last in the future, I propose a notion of equilibrium refinement based on the feature that mining uses real resources. This refinement eliminates all equilibria where the value of the cryptocurrency is zero at some point in time or converges to zero over time. This result suggests that agents can collectively sustain the value of the cryptocurrency using costly mining as a coordinating device.
Fintech modernization is a ground-up shift from traditional batch-processing infrastructure to event-driven real-time architectures that redefine financial service delivery and social mechanisms of trust. Modern financial institutions draw on advanced stream processing technologies, API-first integration, and distributed computing to support transaction throughput rates in millions of operations per second with sub-millisecond latencies for key financial transactions. Occasion-driven architectures (also known as event-driven architecture) provide instantaneous affirmation of transactions, real-time detection of fraud, and clear audit trails through immutable event recording structures that ensure end-to-end transaction traceability for regulatory purposes. Mobile-first design patterns and modern web-based packages boost access to finance for the underprivileged through offline-enabled interfaces that function across diverse device specifications and network connectivity eventualities. Advanced cryptographic algorithms, which include homomorphic encryption and zero-knowledge proofs, facilitate privacy-enhancing analytics that reconcile customized financial offerings in opposition to people’s privacy protection. Regulatory technology embedding using compliance-by-design architectures in regulatory technology help automate policy application and reporting while advanced trust protocols using biometric authentication, behavior analysis, and machine learning algorithms prevent fraud while ensuring frictionless user experiences. The intersection of distributed architectures, privacy-retaining technologies, and inclusive design styles generates financial structures that cater to various populations even as adhering to demanding safety and regulatory compliance in diverse jurisdictions.
Scientific knowledge production is undergoing a dual transformation. On one front, Decentralized Science (DeSci) leverages blockchain-based infrastructures to reconfigure how research is funded, verified, and governed, disintermediating legacy gatekeepers through tokenized incentives and distributed provenance. On the other, Artificial Intelligence (AI) is automating core dimensions of science, from hypothesis generation to experimental execution and model validation. This paper introduces DeScAI, a theoretical framework that unifies these domains into a recursive, self-verifying epistemic system governed by autonomous agents operating within decentralized, trust-minimized networks. We present a five-stratum architecture for DeScAI, hypothesizing that its integration enables epistemic acceleration, pluralistic inquiry, and cryptographically auditable trust. Methods include a structured literature synthesis (2018–2025), conceptual modeling, and descriptive analysis of 14 projects. Three hypothetical trajectories for future empirical investigation are proposed concerning cycle-time compression, epistemic pluralism, and reproducibility amplification. We conclude that DeScAI is not speculative: its core components are already deployed. What remains is orchestration, stitching together decentralized ledgers, incentive protocols, self-sovereign scientific agents (SSA), and cryptographic infrastructures into a single, recursive system. If successful, DeScAI could radically reduce the latency between hypothesis and verification, reconfigure scientific legitimacy as a live, contestable signal, and transform the incentive structure of research itself.
Ch. Rupa, Sai Varshitha. G, D Divya, Thippa Reddy Gadekallu · 6 authors
The world faces a severe blood shortage, with a gap of 1.95 million units, highlighting the need for efficient blood allocation and management systems. Traditional cloud and blockchain approaches have been explored for blood bank management but faced implementation challenges. This study proposes designing and developing a decentralized Binance blockchain-based application framework to ensure transparency and security. It uses the AdaBoost algorithm to predict the availability of the nearest blood bank and blood donor. Supply chain management provides transparency without the intervention of third parties thereby preventing blood crimes. Metamask is incorporated for crypto transactions in the Binance Smart Chain test network (BSC). BSC stands out for its low transaction fees and high scalability, enabling swift transaction processing at a fraction of the cost compared to Ethereum. The smart contracts are deployed using hardhat configuration enabling BscScan as an Application Programming Interface (API) gateway to record transactions within the decentralized application (dApp). The proposed system achieved an accuracy of 99.5%, demonstrating the robustness of the AdaBoost model in predicting blood availability. The integration of blockchain technology ensures transparency, immutability, and secure traceability of blood transactions across the network.
M. Veena, K. Sravani, K. Dhanapal, G. Praveen Kumar · 6 authors
This study was done to ensure effective fisheries management while protecting marine ecosystems and livelihoods by integrating traceability and sustainability practices. Fisheries played a crucial role in food security worldwide, supporting millions of livelihoods and providing nutrition for billions. However, challenges like overfishing, Illegal fishing, and environmental degradation threatened marine ecosystems. Traceability and sustainability were vital to addressing these issues by combating Illegal, Unreported and Unregulated (IUU) fishing, ensuring food safety, supporting sustainable practices, and meeting regulatory requirements. The fish industry experienced substantial illegal, unreported, and unregulated (IUU) activities within traditional supply chain systems. Blockchain technology and the Internet of Things (IoT) were expected to transform the fish supply chain (FSC) by incorporating distributed ledger technology (DLT) to build trustworthy, transparent, decentralized traceability systems that promoted secure data sharing and employed IUU prevention and detection methods. Efficient traceability management was necessary for managing products in the fishery supply chain. Monitoring and tracking of the fishery supply chain operations assisted system stakeholders in identifying the origins and causes of product fraud and malpractice. Traceability helped prevent IUU fishing, which costs up to $23.5 billion annually, by using blockchain systems to record catch data immutably and enable real-time tracking. Technologies like RFID (Radio Frequency Identification tags) and QR (Quick Response) codes verified seafood origin, reducing fraud and enhancing food safety. Sustainability prevented overfishing, preserved biodiversity, and supported small-scale fishers through fair trade practices. With 75% of consumers favouring sustainably sourced seafood, blockchain enhanced trust by providing transparent sustainability records and QR code verification.
This study provides an in-depth analysis of the dynamic connectedness between G7 stock market indices, traditional cryptocurrencies (Bitcoin, Ethereum), gold, digital gold (PAXG, XAUT), and companies specializing in artificial intelligence (AI). Covering the period from 2020 to 2024, the analysis focuses on four distinct periods: the COVID-19 pandemic, the Russia-Ukraine conflict, the banking crisis triggered by the collapse of Silicon Valley Bank in March 2023 and the speculative rise in the gold markets in 2024. The methodology employs a Quantile Vector Autoregressive (QVAR) connectivity approach, starting with the median quantile and systematically extending to various quantiles to capture the entire distribution of connectedness under different market conditions. Our results reveal significant fluctuations in the Total Connectivity Index (TCI) during the studied crises and demonstrate how the roles of key assets—Bitcoin, Ethereum, gold, PAXG, XAUT, and AI firms—shift between being net emitters and receivers of shocks. These shifts underscore the importance of asset selection in crafting effective hedging strategies. Specifically, we observe that G7 investors adopt varying diversification strategies depending on their domestic market conditions and the specific crisis period. The study highlights that assets for diversification and risk reduction vary by country and crisis. Traditional cryptocurrencies and AI companies in general emerge as promising diversification tools, especially in times of technological disruption and economic uncertainty. Several financial implications for investors and policymakers are proposed, providing insights for optimizing portfolio resilience in the face of global market volatility.
This study examines links between global financial stress and cryptocurrency returns from 1 January 2017 to 31 January 2025, while explicitly accounting for commodity markets. We use an econometric toolkit: unit-root and cointegration testing, ARDL bounds, Toda–Yamamoto causality, and a two-state Markov Switching model to trace long-run equilibrium and transmission mechanisms across cryptocurrencies (BGCI), systemic stress (OFR-FSI), volatility measures (VIX, VVIX, VSTOXX, VVSTOXX, MOVE), major equities and bonds, and three commodities (gold, oil, copper). Results show robust long-run cointegration between BGCI and several financial variables, including S&P/ASX 200 and the Bloomberg Barclays Bond Index; models that include commodities continue to support these long-term links. Toda–Yamamoto tests reveal that stress and volatility indices unidirectionally transmit shocks to cryptocurrencies and commodities, while gold displays a bidirectional relationship with BGCI, indicating a conditional safe haven interaction. Markov Switching estimates show amplified co-movement among BGCI, gold and bonds in stress regimes, with the model predominantly remaining in a normal state. Overall, cryptocurrencies are embedded within the broader financial system; commodities, especially gold, are used to moderate the stress crypto transmission and offer conditional diversification value during turmoil.
Portfolio optimization is a cornerstone of modern financial decision-making, tradition-ally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return max-imization, mean absolute deviation (MAD) minimization, and entropy-based diversifi-cation into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average mag-nitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and en-tropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved di-versification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allo-cation, and decentralized finance, especially under high-uncertainty market conditions.
Muhammad Farooq Shaikh, S. Hamza Hassan, Alessia Maccaro, Giacomo Pratesi · 5 authors
The proposed model in this paper focuses on asset tracking and monitoring in the healthcare industry and it uses blockchain technology. Data security incidents in the healthcare field have created financial and ethical problems over the last few years. During 2024, the money lost from healthcare data breaches often exceeded $4.88 million due to the exposure of private patients and asset information. This shows why it is important to use secure systems to protect worthwhile information and manage key assets efficiently and correctly. Since such intrusions cannot be prevented by the current systems, businesses end up losing money and working less efficiently. These issues are addressed by using a system that includes blockchain, IoT and digital asset tracking technologies. To ensure data integrity and prevent fake information, Proof of Authority (PoA) uses chosen, recognized authorities to verify and confirm each transaction. To protect transactions, PoA requires a number of approvals from different parties which helps ensure that transactions are safe and secure. With immutable and decentralized features, blockchain makes the management of assets more secure and transparent. Records of asset transfers and data are safely stored on the blockchain with smart contracts, providing real-time monitoring and no room for errors. When integrated with IoT devices, the system can constantly check all the assets, improving the company's efficiency while reducing losses of items. The findings suggest that a PoA blockchain system can help healthcare asset management systems operate more ethically, safely, transparently, and efficiently.
Abstract This study provides essential insights into how diffusion processes unfold in complex networks, with a focus on cryptocurrency blockchains and infrastructure networks. The structural properties of these networks, such as hub-dominated, heavy-tailed topology, network motifs, and node centrality, significantly influence diffusion speed and reach. Using epidemic diffusion models, specifically the Kertesz threshold model and the Susceptible-Infected (SI) model, we analyze key factors affecting diffusion dynamics. To assess the uncertainty in the fraction of infected nodes over time, we employ bootstrap confidence intervals, while Bayesian credible intervals are constructed to quantify parameter uncertainties in the SI models. Our findings reveal substantial variations across different network types, including Erdős-Rényi networks, Geometric Random Graphs, and Delaunay Triangulation networks, emphasizing the role of network architecture in failure propagation. We identify that network motifs are crucial in diffusion. We highlight that hub-dominated networks, which dominate blockchain ecosystems, provide resilience against random failures but remain vulnerable to targeted attacks, posing significant risks to network stability. Furthermore, centrality measures such as degree, betweenness, and clustering coefficient strongly influence the transmissibility of diffusion in both blockchain and critical infrastructure networks.
Adversarial smart contracts, mostly on EVM-compatible chains like Ethereum and BSC, are deployed as EVM bytecode to exploit vulnerable smart contracts for financial gain. Detecting such malicious contracts at the time of deployment is an important proactive strategy to prevent losses from victim contracts. It offers a better cost-benefit ratio than detecting vulnerabilities on diverse potential victims. However, existing works are not generic with limited detection types and effectiveness due to imbalanced samples, while the emerging LLM technologies, which show their potential in generalization, have two key problems impeding its application in this task: hard digestion of compiled-code inputs, especially those with task-specific logic, and hard assessment of LLM's certainty in its binary (yes-or-no) answers. Therefore, we propose a generic adversarial smart contracts detection framework FinDet, which leverages LLM with two enhancements addressing the above two problems. FinDet takes as input only the EVM bytecode contracts and identifies adversarial ones among them with high balanced accuracy. The first enhancement extracts concise semantic intentions and high-level behavioral logic from the low-level bytecode inputs, unleashing the LLM reasoning capability restricted by the task input. The second enhancement probes and measures the LLM uncertainty to its multi-round answering to the same query, improving the LLM answering robustness for binary classifications required by the task output. Our comprehensive evaluation shows that FinDet achieves a BAC of 0.9374 and a TPR of 0.9231, significantly outperforming existing baselines. It remains robust under challenging conditions including unseen attack patterns, low-data settings, and feature obfuscation. FinDet detects all 5 public and 20+ unreported adversarial contracts in a 10-day real-world test, confirmed manually.
The proliferation of unmanned aerial vehicle (UAV) swarms in mission-critical applications for 6G and the Internet of Things (IoT) introduces significant security vulnerabilities stemming from their dynamic, distributed, and resource-constrained nature. Traditional security paradigms are often inadequate for these complex cyber-physical systems. This paper proposes a novel, cross-layer security framework that ensures robust and lightweight operation for UAV swarms. The framework is founded on a novel Entropy-Derived Physically Unclonable Function (EPUF) based on DRAM, which employs a data-driven characterization process designed to achieve near 100% reliability in simulation through a data-driven characterization process, which is validated through extensive simulation, addressing a critical limitation of conventional PUFs. To counteract sophisticated threats, we formulate the key management problem as a Markov Decision Process (MDP) and introduce a deep reinforcement learning (DRL) agent that dynamically optimizes key update frequency, balancing security posture against energy consumption. Furthermore, we leverage a lightweight, permissioned blockchain as a decentralized trust anchor for public key management, providing an immutable and resilient ledger and enhancing the principles of distributed and edge intelligence. The core authentication protocol's security is formally verified using the ProVerif tool and Belief Logic, proving its robustness against a Dolev-Yao adversary. Experimental simulations demonstrate that our framework significantly outperforms conventional methods, reducing authentication latency and energy consumption by over 95% compared to PKI-based schemes while effectively mitigating replay and impersonation attacks.