Sextortion has rapidly expanded into a global cyber-enabled crime that leverages anonymous digital communication and decentralized payment systems. This study examines the financial infrastructures underlying contemporary sextortion by conducting a two-phase analysis of 87 confirmed cases involving cryptocurrency payments. Using blockchain forensic tools and open-source intelligence, the research traces fund movements across perpetrator-controlled wallets, identifies laundering techniques such as mixers, peel-chain transfers, and exchange-based cash-outs, and links these behaviors to narrative patterns within victim reports. The results reveal a dual-tier ecosystem in which mass-produced, multilingual extortion scripts coexist with divergent laundering typologies that differentiate lower-value, high-volume scams from more organized and higher-yield operations. By integrating qualitative and quantitative evidence, this study provides a forensic framework for detecting illicit cryptocurrency activity, improving threat classification, and strengthening investigative and regulatory responses to sextortion and related crypto-enabled interpersonal crimes.
BACKGROUND India’s health systems reform journey has been marked by institutional innovations that have reshaped service delivery, governance, financing, and beyond. Among these, a foundational yet often overlooked innovation is the creation of a structured ecosystem for health policy guidance: a network of State Health Systems Resource Centres (SHSRCs), supported by the National Health Systems Resource Centre (NHSRC). These institutions were not intended as parallel implementation units. Rather, they were envisioned as embedded policy advisory bodies that are intended to synthesize evidence, support strategic planning, and enable system-wide reforms. While NHSRC continues to serve as the apex technical institution supporting the Ministry of Health and Family Welfare (MoHFW), the SHSRCs were designed to play a decentralized and synergistic role within states. However, they remain variably recognized and underutilized. Unlocking their full potential could substantially enhance the capacity for state-level, evidence-informed decision-making and strategic design. AN INSTITUTIONAL DESIGN WITH PURPOSE Established in 2007, NHSRC functions as the principal technical support agency for MoHFW, with a mandate that includes policy and strategy development, technical assistance to states, and capacity building under the National Health Mission (NHM). Over time, it has played a pivotal role in institutionalizing quality improvement processes, advancing health financing reforms, guiding human resource strategies, strengthening secondary care and governance, innovations in community processes, and improved primary health care. Its enhanced role over the past 5 years, particularly through expanded expertise in evidence generation, implementation research, and the information technology realm, has been well appreciated and acknowledged. The SHSRCs, supported under the NHM and guided by NHSRC, were first envisioned under the National Rural Health Mission as in-house technical institutions to support health systems strengthening and policy development, particularly in the Empowered Action Group states.[1] However, their formation varies widely. Maharashtra and Madhya Pradesh, for instance, have established autonomous SHSRCs with independent governance and operational flexibility. Others, like Kerala, have adopted a fully embedded model within the state health department, with no legal autonomy but strong proximity to decision-making. Gujarat has adopted a hybrid approach, combining knowledge management cells, technical support functions, and programmatic units aligned with NHM priorities. In Chhattisgarh, the model transitioned from a registered society to an outsourced public–private partnership structure. Newer entrants like Meghalaya illustrate growing development partner involvement in SHSRC functions through philanthropic support. In the absence of a unified design, this diversity has led to fragmentation in roles, mandates, and institutional identity. To address this, the MoHFW released a national SHSRC Framework in 2024, formalizing key principles of governance, technical leadership, and accountability.[2] The framework aims to guide states in repositioning SHSRCs as embedded policy support institutions that are context-specific yet aligned with national health priorities. AN UNEVEN LANDSCAPE OF UTILIZATION Despite the clarity of this institutional design, the operational landscape of SHSRCs across India remains uneven. While some centers have emerged as credible partners to their state governments, others face challenges ranging from intermittent staffing and fragmented mandates to unclear positioning within state bureaucracies. In several instances, donor-funded Technical Support Units (TSUs) have taken on overlapping roles. These arrangements may address immediate programmatic needs but often lack the institutional continuity, embedded authority, and public accountability required for long-term reform.[3] Overreliance on donor-funded TSUs risks fragmenting institutional ownership and accountability, weakening the state’s own capacity to generate and use evidence for policy guidance. Recognizing these risks, NHSRC has begun working with state governments to revitalize SHSRCs and help align them with national and state-level priorities, while safeguarding their role as government-owned and state-anchored policy advisory bodies. EARLY EVIDENCE OF WHAT WORKS Where SHSRCs have been clearly institutionalized, their contributions to health policy and systems strengthening are evident. In Chhattisgarh, SHSRC was central to the design and implementation of the Mitanin program, which later became the foundation for the national ASHA model. Its positioning as a public, in-house technical agency enabled long-term continuity, responsiveness to state-specific challenges, and innovation uptake.[4] In Odisha, the SHSRC has supported district health planning, capacity building, and institutional development initiatives across program areas. In Tamil Nadu, it has supported quality assurance mechanisms and monitoring systems within the health department. These cases suggest that, when adequately structured and supported, SHSRCs can serve as trusted intermediaries that connect evidence, program strategy, and systemic reform. STRENGTHENING SHSRCS FOR HEALTH POLICY GUIDANCE For SHSRCs to fulfil their intended role as policy advisory institutions, four strategic actions are necessary. First, states must clearly define the mandate and governance of SHSRCs based on the MoHFW’s framework. This includes formalizing their distinct identity from TSUs, clarifying reporting structures, and embedding them within state health departments with a long-term vision. Second, sustainable financing should be assured through NHM provisions to reduce dependence on external actors. While TSUs may continue to serve specialized programmatic functions, they should not be equated as substitutes for in-house capacity. Third, investment in technical leadership and multidisciplinary staffing is essential. SHSRCs must attract professionals across epidemiology, public finance, implementation research, health systems, and data analytics. These are all disciplines critical to robust policy guidance. Establishing leadership structures that ensure continuity and accountability will further enhance operational coherence and effectiveness. Fourth, SHSRCs should continually expand their engagement with emerging health system interventions and institutionalize mechanisms. This includes supporting research, evaluation, and evidence-based decision-making. Their potential as platforms for resource optimization and collaboration with academic and public health institutions remains significantly underleveraged. NHSRC, through its existing mandate, can continue to play a catalytic role in this transformation by facilitating peer learning, technical handholding, and capacity strengthening. A STRATEGIC ASSET FOR THE NEXT PHASE OF REFORM As India deepens its health system reforms through initiatives such as Ayushman Bharat, and ongoing programme interventions under NHM, the need for decentralized, embedded policy guidance becomes more urgent. SHSRCs are already positioned to fulfil this role, not as supplementary structures but as enduring public institutions grounded in local systems and aligned with national goals. The imperative now is not to create new structures but to recognize and invest in the institutional capacities already in place. Authors’ contributions Maj. Gen. (Prof) Dr Atul Kotwal: Conceptualization; Writing – Original Draft, Writing – Review and Editing; Supervision. Dr Tarannum Ahmed: Conceptualization; Writing – Original Draft, Writing – Review and Editing. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest to declare.
This study examines how fiscal governance structures influence corporate environmental performance by exploiting China’s 2003 tax delegation reform as a quasi-natural experiment. The reform transferred corporate income tax collection authority from locally-governed Local Tax Bureaus (LTBs) to centrally-managed State Tax Bureaus (STBs) based on a firm registration date cutoff of January 1, 2002. Using a Regression Discontinuity Design (RDD) with micro-level panel data from Chinese manufacturing firms (2004-2008), we identify the causal impact of tax administration assignment on firm-level energy efficiency, measured as output per unit of energy consumed. Our findings reveal that firms under LTB administration exhibit 8-12% higher energy efficiency compared to comparable firms under STB administration. This effect persists across multiple robustness checks, including alternative bandwidth specifications, placebo tests using unaffected firms, and alternative energy efficiency measures. Mechanism analysis demonstrates that the energy efficiency gains stem from three primary channels: (1) relaxed financial constraints enabling greater investment capacity, (2) transition toward cleaner energy sources with reduced coal dependency, and (3) increased adoption of energy-saving technologies and green innovation. These effects are particularly pronounced among financially constrained firms, non-exporters, and firms in regions with higher fiscal capacity or stronger environmental pressure. These results contribute to three strands of literature. First, they provide novel evidence that fiscal administrative structures—traditionally viewed as purely revenue instruments—can have substantial unintended environmental consequences. Second, they demonstrate how local fiscal flexibility may create conditions conducive to green technological upgrading by alleviating financial frictions. Third, they extend the Porter Hypothesis to the institutional level, showing that supportive governance arrangements can simultaneously enhance economic efficiency and environmental sustainability. The findings suggest that integrating environmental performance metrics into local tax administration evaluation frameworks could align fiscal incentives with sustainability objectives, offering a promising pathway for emerging economies to achieve coordinated economic and environmental goals. • LTB oversight improves firm energy efficiency by 8–12% over STB control. • Lenient tax enforcement eases financing constraints for cleaner energy adoption. • Environmental benefits are stronger in fiscally surplus or high-pressure cities. • Financing-constrained and non-exporting firms benefit most from LTB regulation. • Study links decentralized tax control to unexpected environmental improvements.
Abstract This paper investigates whether Bitcoin serves as a safe haven and a diversification tool for both developed and emerging stock markets during the COVID-19 crisis, in comparison with gold. The analysis covers daily data from June 18, 2012, to May 25, 2020, across a representative set of developed (S&P500, FTSE100, DAX, CAC40, Nikkei225, Ibex35) and emerging (Shanghai, Nifty50, Ibovespa, MOEX) equity markets, providing a comprehensive view of asset interactions in different financial environments. Methodologically, we employ a two-step approach: an EGARCH model to estimate time-varying volatility, followed by a copula-based framework to capture nonlinear and asymmetric dependence structures. This combination allows for a nuanced assessment of asset behavior under both tranquil and crisis conditions. The results show that Bitcoin maintains weak dependence on developed equity markets during the COVID-19 period but fails to display consistent safe-haven characteristics under extreme stress. Gold, by contrast, continues to act as a reliable hedge, confirming its traditional role in protecting portfolios against market downturns. Overall, these findings suggest that while Bitcoin may provide diversification benefits under normal circumstances, it cannot yet replace gold as a robust safe-haven asset. For portfolio managers, this highlights the importance of gold in risk management, while underscoring Bitcoin’s evolving yet still uncertain role in global financial markets.
Yaser Sadati-Keneti, Mohammad Vahid Sebt, Reza R. Tavakkoli-Moghaddam, Orod Ahmadi
The aim of this research is to employ improved machine learning techniques to determine the best Bitcoin trading positions in response to sudden price changes caused by global emergencies such as pandemics, conflicts, and economic disputes. Specifically, this study examines price fluctuations during the COVID pandemic as a case study to evaluate the performance of the algorithms investigated. We present a novel hybrid approach that merges Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Decision Tree (DT) classification to effectively eliminate noisy data and extract pertinent information for accurate position forecasting. The DBSCAN algorithm organizes the data to reveal important patterns, while the DT classifier sorts the trading signals. The performance of the proposed DBSCAN-DT model is rigorously compared with established alternatives, including the Multi-Layer Perceptron (MLP), Support Vector Classifier (SVC), and traditional Decision Trees. Findings from the experiments show that the DBSCAN-DT hybrid consistently outperforms these benchmarks during the outbreak, epidemic, and pandemic phases of COVID, attaining greater accuracy in forecasting both trading positions and market trends. These findings emphasize the essential importance of incorporating pandemic-related disruptions into cryptocurrency price prediction models and showcase the flexibility of our method in addressing sudden market changes.
Digital networks governed by attention-economy algorithms exhibit accelerating entropic decay, manifesting as cognitive fragmentation and systemic instability. This paper posits a negentropic imperative for socio-technical design, synthesizing Schrödinger’s physics of life with Stiegler’s neganthropology and Floridi’s information ethics. It proposes a model of metabolic networks to counteract this decay, moving beyond the extractive Creator Economy toward a Contributor Economy. The model’s core mechanisms include a Uniqueness Quotient (replacing engagement metrics with singularity valuation), Tokenized Value Exchange (creating non-fungible, context-specific value flows), and AI-as-Negentropic-Ally protocols. Grounded in information theory, this framework offers an architectural alternative to platform capitalism, aiming to foster cognitive sustainability, decentralized value cultivation, and collective individuation against the entropic trajectory of algorithmic dis/order.
Fatemeh Erfan, Mohammad Yahyatabar, Martine Bellaïche, Talal Halabi
• Created a refined, expanded, and precisely labeled dataset with explanations, risk assessments, and fixes for each vulnerability • Fine-tuned an open-source LLM: LLaMA-3.1-8B using parameter-efficient techniques (LoRA) for smart contract vulnerability detection • Fine-tuned GPT-4o-mini on the same corpus for comparative analysis • Developed a real-time Visual Studio Code (VSCode) plugin integrating GPT-4o-mini for smart contract auditing • Released the datasets, tool, and fine-tuned model to advance research in smart contract security Since the advent of Ethereum, ensuring the security of smart contracts has become imperative. Integer overflow and underflow, reentrancy, and timestamp dependency remain the three most prevalent vulnerabilities in deployed contracts. Existing static-analysis tools often yield insufficient accuracy, and datasets derived from them inherit the same shortcomings. Moreover, the smart contract ecosystem lacks a dependable, real-time auditing aid for developers and a fine-tuned model trained on a truly comprehensive corpus. In this paper, we present three main contributions. (1) Dataset curation: the state-of-the-art vulnerability datasets are aggregated and harmonized, producing a clean, fully labeled dataset that integrates detailed explanations, potential security risks, vulnerable line ranges, code snippets, and corresponding fixes. The dataset is publicly available via our GitHub repository. (2) Model fine-tuning: the LLaMA-3.1-8B model as well as GPT-4o-mini are fine-tuned on this corpus and evaluated with both standard classification metrics and text-quality measures. The fine-tuned LLaMA-3.1 model achieves a precision of 93.55%, an average semantic similarity of 77.48%, and a code similarity of 87.25%. (3) IDE integration: We implement a real-time Visual Studio Code extension, backed by the GPT-4o API, that highlights, explains, and automatically patches vulnerabilities as the developer writes. Together, these contributions deliver a rigorously validated model and a practical developer toolchain that markedly advance the state of smart contract security research and practice.
The growth of digital music streaming platforms has changed the way music is distributed and accessed across the world. These platforms make music easily available to listeners the royalty distribution process still faces several challenges. Limited transparency, delayed payments, and the involvement of multiple intermediaries often reduce efficiency and affect the earnings received by artists. This work shows a blockchain-based framework for music royalty distribution. The given system combines Ethereum smart contracts, Non-Fungible Tokens (NFTs), and the Inter Planetary File System (IPFS) to support secure ownership management and automated royalty payments. Smart contracts execute royalty transactions based on predefined conditions, while NFTs represent ownership of digital music files. IPFS is used for decentralized storage to maintain secure and tamper resistant media files. By reducing dependency on central authorities, the framework makes royalty transactions easier to track and supports fair revenue distribution for artists. The obtained results show fast royalty processing and improved revenue sharing compared with old royalty management systems [18].
Smart contracts (SCs) implemented on blockchain represent a breakthrough in decentralized applications, enabling a range of functions such as managing supply chains and handling elections. As the adoption of SCs increases, the need to detect flaws and vulnerabilities in their execution grows. To address this challenge, we present Branch Reinforcement Learning Fuzzer (BRLF), a deep reinforcement learning-based solution for the detection of vulnerabilities in SCs. The novelty of our method is threefold: first, our deep model uses text-based embeddings of conditional branches to enhance its adaptability and flexibility. Secondly, we propose a reward function that considers multiple aspects of fuzzing, such as opcode analysis and gas usage. Finally, we incorporate evolutionary algorithms into our approach, which significantly bolsters its ability to produce varied outputs. Extensive evaluation on three datasets of Ethereum-based SCs shows that BRLF outperforms state-of-the-art methods, detecting more vulnerabilities and achieving higher code coverage than existing solutions. Our code and data are available at: https://zenodo.org/records/15022152
Tsvetelina Ivanova, L Koleva, Idilia Batchkova, G Kolev
Abstract Reliable vacuum control in high-precision installations such as the Electron Beam Melting and Refining (EBMR) plant requires an intelligent architecture that integrates physical subsystems and cyber entities under an adaptive control framework. This paper proposes a multi-agent system (MAS) representation of the EBMR vacuum creation subsystem, developed using the Organizational Multi-Agent Systems Engineering (O-MaSE) methodology. The model unites the IEC 61512 (S88) batch-process standard with the IEC 61499 distributed-control architecture to form a modular and interoperable cyber-physical system (CPS). Each pump, valve, and sensor is modelled as an autonomous agent with defined goals, roles, and communication protocols. The O-MaSE-based design enhances scalability, fault tolerance, and system adaptability, enabling decentralized decision-making and efficient vacuum regulation. The integrated case study demonstrates that MAS-based CPS design substantially improves the responsiveness and resilience of EBMR operations, supporting the principles of Industry 4.0.
Hybrid fuzzing combines greybox fuzzing's throughput with the precision of symbolic execution to uncover deep smart contract vulnerabilities. However, its effectiveness is often limited by constraint pollution: in real world contracts, path conditions pick up semantic noise from global state and defensive checks that are syntactically intertwined with, but semantically peripheral to, the target branch, causing SMT timeouts. We propose NeuroSCA (Neuro-Symbolic Constraint Abstraction), a lightweight framework that selectively inserts a Large Language Model (LLM) as a semantic constraint abstraction layer. NeuroSCA uses the LLM to identify a small core of goal-relevant constraints, solves only this abstraction with an SMT solver, and validates models via concrete execution in a verifier-in-the-loop refinement mechanism that reintroduces any missed constraints and preserves soundness. Experiments on real-world contracts show that NeuroSCA speeds up solving on polluted paths, increases coverage and bug-finding rates on representative hard contracts, and, through its selective invocation policy, achieves these gains with only modest overhead and no loss of effectiveness on easy contracts.
Smart contracts underpin high-value ecosystems such as decentralized finance (DeFi), yet recurring vulnerabilities continue to cause losses worth billions of dollars. Although numerous security analyzers that detect such flaws exist, real-world attacks remain frequent, raising the question of whether these tools are truly effective or simply under-used due to low developer trust. Prior benchmarks have evaluated analyzers on synthetic or vulnerable-only contract datasets, limiting their ability to measure false positives, false negatives, and usability factors that drive adoption. To close this gap, we present a mixed-methods study that combines large-scale benchmarking with practitioner insights. We evaluate six widely used analyzers (i.e., Confuzzius, Dlva, Mythril, Osiris, Oyente, and Slither) on 653 real-world smart contracts that cover three high-impact vulnerability classes from the OWASP Smart Contract Top Ten (i.e., reentrancy, suicidal contract termination, and integer arithmetic errors). Our results show substantial variation in accuracy (F1 = 31.2 to 94.6%), high false-positive rates (up to 32.6%), and runtimes exceeding 700 seconds per contract. We then survey 150 professional developers and auditors to understand how they use and perceive these tools. Our findings reveal that excessive false positives, vague explanations, and long analysis times are the main barriers to trust and adoption in practice. By linking measurable performance gaps to developer perceptions, we provide concrete recommendations for improving the precision, explainability, and usability of smart-contract security analyzers.
This review explores the application of machine learning techniques for fraud detection and prevention in the Ethereum blockchain. As a leading platform for decentralized applications (dApps), Ethereum is vulnerable to fraudulent activities such as scams, hacking attempts, and malicious transactions. This paper provides a comprehensive analysis of machine learning models used to predict, detect, and mitigate fraudulent behavior within the Ethereum ecosystem. By overviewing various machine learning methods, this study identifies the most effective approaches for addressing different types of vulnerabilities while offering a thorough review of existing research, key challenges, and limitations. It also examines the datasets and feature engineering techniques applied in this domain, outlining future directions and potential strategies for improving fraud detection. While machine learning has enhanced Ethereum’s security, challenges such as data availability, adversarial attacks, and model interpretability remain significant concerns. To address these gaps, this study highlights the potential of integrating deep learning architectures, graph representations, and hybrid models that combine supervised and unsupervised learning. Additionally, it explores the use of active learning and genetic programming to further enhance fraud detection capabilities. Furthermore, leveraging AI, particularly through large language models, could improve interpretability at the account, block, or transaction level, offering a clearer, more comprehensive view of fraudulent behavior across the Ethereum network. By tackling these challenges, future advancements in machine learning could further strengthen the resilience, security, and trustworthiness of Ethereum’s infrastructure.
Blockchain has gained significant attention in recent years, with smart contracts enabling automated and trustless financial interactions such as decentralized exchanges, tokenized assets, and on-chain governance. Because these programs often control assets of substantial value, a large body of research has focused on detecting security vulnerabilities in smart contracts. However, beyond security, understanding the actual behavior of a contract remains challenging, particularly when source code is unavailable. This work addresses the identification of semantic properties, defined as behavior-level characteristics that describe a contract's purpose based on its state changes and interactions. Detecting such properties can support applications such as regulatory analysis of relevant contracts and the simplification of contract logic by filtering semantically uninformative boilerplate code. The work focuses on static analysis approaches utilizing Datalog.The methodology first includes a systematic literature review to identify existing approaches for the static analysis of Ethereum bytecode using Datalog, as well as related work on semantic properties. The review indicates that Gigahorse is currently the most prominent tool in this category; consequently, it was selected as the basis for the following analysis.Based on this foundation, several function-level properties are defined, including authenticated functions as well as different types of setter and getter functions. In addition, a contract-level property representing a simple token contract is defined. Detection mechanisms for these properties are implemented in Datalog and subsequently evaluated. The results indicate that Gigahorse is generally well-suited for detecting such semantic properties, although practical limitations were encountered, particularly in the form of limited or missing documentation.
This study evaluates the robustness of Proof of Team Sprint (PoTS) against adversarial attacks through simulations, focusing on both the attacker win rate and computational efficiency under varying team sizes (N) and attacker ratios (α). PoTS is a recently proposed consensus mechanism that relies on randomly formed teams of participants to collaboratively generate blocks. Unlike traditional consensus methods where individual nodes compete independently, PoTS distributes responsibility across multiple nodes in a team, thereby increasing resilience against coordinated attacks. Our simulation results demonstrate that PoTS effectively reduces an attacker’s ability to dominate the consensus process, even under challenging conditions. For instance, when α ＝ 0.5, the attacker win rate decreases from 50.7% at N ＝ 1 to below 0.4% at N ＝ 8, effectively neutralizing adversarial influence. Similarly, at α ＝ 0.8, the attacker win rate drops from 80.47% at N ＝ 1 to only 2.79% at N ＝ 16, highlighting PoTS’s robustness under extreme threat levels. In addition to its strong security properties, PoTS maintains high computational efficiency by synchronizing block generation within each team. We introduce the concept of Normalized Computation Efficiency (NCE) to quantify this efficiency gain, demonstrating that PoTS significantly improves resource utilization as team size increases. As N grows, PoTS not only enhances security but also achieves better computational efficiency due to the averaging effects of execution time variations among team members. These findings underscore PoTS as a promising and practical alternative to traditional consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS). By leveraging team-based block generation, sequential execution, and randomized participant reassignment in each round, PoTS provides a scalable, resilient, and energy-efficient framework for decentralized consensus in blockchain systems.
Nach dem anfänglichen Hype um die Blockchain-Technologie, die erstmals durch Satoshi Nakamotos Bitcoin bekannt wurde, hat sich der Bereich in Richtung der Entwicklung ausgereifter Blockchain-basierter Systeme und Anwendungen weiterentwickelt. In dieser weitläufigen Landschaft fungieren die einzelnen Blockchain-Plattformen und Ökosysteme häufig als isolierte Silos, die strikt von anderen Plattformen getrennt sind und über keine inhärenten Interoperabilitätsfunktionen verfügen. Trotz der Existenz etablierter Mechanismen für den Austausch von Coins und Tokens über heterogene Blockchains hinweg müssen Entwickler von Web3-Anwendungen, die aus Smart Contracts bestehen, möglicherweise auf individuelle Anpassungen zurückgreifen, um Blockchain-übergreifende Anwendungen zu ermöglichen. In vielen Fällen sind diese Ansätze nicht ausreichend skalierbar, wenn die Anwendung auf zusätzlichen Blockchain-Plattformen verteilt werden muss. Folglich sind weitere Anpassungen erforderlich. Darüber hinaus stellt sich die Frage der Speicherung gemeinsamer Anwendungsdaten, die mit Smart Contracts kompatibel und für das dezentrale Konzept der Blockchain geeignet sein muss. Diese Arbeit präsentiert einen Vorschlag für eine Blockchain-übergreifende Datenspeicherlösung, die das InterPlanetary File System (IPFS) als dezentrale Off-Chain-Persistenzschicht und Blockchain-Oracles nutzt, um Lese- und Schreibvorgänge zu ermöglichen. Der Einsatz von incentivierten Vermittlern in Verbindung mit einem neuartigen Oracle-Verifizierungsmechanismus für Schreibzugriffe erlaubt die Formulierung eines Lösungsentwurfs für ein vollständig dezentrales System. Dieser Ansatz ermöglicht die lose gekoppelte Verbindung von Blockchain-übergreifenden Anwendungen, wobei die einzelnen Blockchain-Plattformen nicht direkt aufeinander zugreifen müssen. Wir präsentieren eine prototypische Implementierung des Lösungsentwurfs und bewerten anschließend den Prototyp hinsichtlich Kosten, Leistung und Sicherheit. Im Vergleich zu einer hypothetischen Referenzlösung, die eine zentralisierte Persistenzschicht verwendet, zeigen wir, dass vollständige Dezentralisierung die Betriebskosten und die Leistung sowie die Integrität der gemeinsam genutzten Daten erheblich negativ beeinträchtigt.
Global disruptions, such as the COVID-19 pandemic, have exposed the fragility of supply chains and the critical need for coordination. However, effective collaboration is often hindered by the reluctance of firms to disclose sensitive proprietary data, such as inventory levels or logistical bottlenecks, due to competitive concerns. To resolve this dilemma, this study introduces a privacy-preserving framework integrating Zero-Knowledge Proofs (ZKPs) with blockchain technology. This approach allows stakeholders to validate compliance and operational status without revealing the underlying raw data, thereby fostering trust and resilience in decentralized networks.
This paper examines the most common smart contracts security issues included in the OWASP Smart Contract Top 10. The purpose of the study is to synthesize a set of recommendations that can help eliminate these key weaknesses or mitigate associated risks. The relevance of this research stems from the rapid development of Web3 technologies, particularly the expanding use of smart contracts. According to various estimates, this market is expected to grow at a CAGR of approximately 25% in the medium term. Furthermore, another factor contributing to the relevance of this topic in Russia is the lack of comprehensive regulation for this class of instruments, especially concerning security requirements and compliance verification. This paper proposes a smart contracts lifecycle model best suited to the research context, describing each stage with particular attention to its impact on security. Existing security weaknesses classifiers specific to smart contracts are identified, with a detailed review of the ten most common vulnerability classes. Based on this review, recommendations are provided to prevent these vulnerabilities or mitigate their associated risks. The findings can be applied by both smart contract developers and security auditors. Additionally, the presented materials contribute to the development of a methodological framework for addressing regulatory issues in the industry.
Autonomous software agents on blockchains solve distributed-coordination problems by reading shared ledger state instead of exchanging direct messages. Liquidation keepers, arbitrage bots, and other autonomous on-chain agents watch balances, contract storage, and event logs; when conditions change, they act. The ledger therefore functions as a replicated shared-state medium through which decentralized agents coordinate indirectly. This form of indirect coordination mirrors what Grassé called stigmergy in 1959: organisms coordinating through traces left in a shared environment, with no central plan. Stigmergy has mature formalizations in swarm intelligence and multi-agent systems, and on-chain agents already behave stigmergically in practice, but no prior application-layer framework cleanly bridges the two. We introduce Indirect coordination grounded in ledger state (Coordinación indirecta basada en el estado del registro contable) as a ledger-specific applied definition that maps Grassé's mechanism onto distributed ledger technology. We operationalize this with a state-transition formalism, identify three recurring base on-chain coordination patterns (State-Flag, Event-Signal, Threshold- Trigger) together with a Commit-Reveal sequencing overlay, and work through a State-Flag task-board example to compare ledger-state coordination analytically with off-chain messaging and centralized orchestration. The contribution is a reusable vocabulary, a ledger-specific formal mapping, and design guidance for decentralized coordination over replicated shared state at the application layer.
Blockchain provides a decentralized and tamper-resistant ledger for securely recording transactions across a network of untrusted nodes. While its transparency and integrity are beneficial, the substantial storage requirements for maintaining a complete transaction history present significant challenges. For example, Ethereum nodes require around 23TB of storage, with an annual growth rate of 4TB. Prior studies have employed various strategies to mitigate the storage challenges. Notably, COLE significantly reduces storage size and improves throughput by adopting a column-based design that incorporates a learned index, effectively eliminating data duplication in the storage layer. However, this approach has limitations in supporting chain reorganization during blockchain forks and state pruning to minimize storage overhead. In this paper, we propose COLE$^+$, an enhanced storage solution designed to address these limitations. COLE$^+$ incorporates a novel rewind-supported in-memory tree structure for handling chain reorganization, leveraging content-defined chunking (CDC) to maintain a consistent hash digest for each block. For on-disk storage, a new two-level Merkle Hash Tree (MHT) structure, called prunable version tree, is developed to facilitate efficient state pruning. Both theoretical and empirical analyses show the effectiveness of COLE$^+$ and its potential for practical application in real-world blockchain systems.
Decentralized financial platforms rely heavily on Web of Trust reputation systems to mitigate counterparty risk in the absence of centralized identity verification. However, these pseudonymous networks are inherently vulnerable to adversarial behaviors, such as Sybil attacks and camouflaged fraud, where malicious actors cultivate artificial reputations before executing exit scams. Traditional anomaly detection in this domain faces two critical limitations. First, reliance on naive statistical heuristics (e.g., flagging the lowest 5% of rated users) fails to distinguish between victims of bad-mouthing attacks and actual fraudsters. Second, standard Graph Neural Networks (GNNs) operate on the assumption of homophily and cannot effectively process the semantic inversion inherent in signed (trust vs. distrust) and directed (status) edges. We propose TAS-GNN (Topology-Aware Signed Graph Neural Network), a novel framework designed for feature-sparse signed networks like Bitcoin-Alpha. TAS-GNN integrates recursive Web-of-Trust labeling and a dual-channel message-passing architecture that separately models trust and distrust signals, fused through a Status-Aware Attention mechanism. Experiments demonstrate that TAS-GNN achieves state-of-the-art performance, significantly outperforming existing signed GNN baselines.
Yao Wu, Ziye Jia, Jingjing Zhao, Haoyang Wang · 6 authors
Unmanned aerial vehicle (UAV) networks are increasingly deployed for complex missions, including disaster response, intelligent logistics, and environmental monitoring. These missions generally require coordinated collaboration among multiple UAVs across distinct administrative domains. To support such cross-domain cooperation, service function chains (SFCs) are constructed, where complex workflows are decomposed into ordered service functions assigned to appropriate UAVs along the mission path. However, it is challenging to ensure secure, trustworthy, and low-latency cross-domain SFC orchestration in identity management, authentication, and resilience to node failures. To address these issues, this paper proposes a consortium blockchain-based trust architecture for cross-domain decentralized identity verification, auditable task execution, and dynamic service-aware orchestrator selection. The framework employs a hierarchical four-phase cross-domain authentication protocol covering the credential pre-verification, intra-domain execution, secure relay, and audit logging. The use case analysis confirms that the proposed framework achieves substantial reductions in authentication latency and significant improvements in system throughput against centralized and static schemes. The open challenges in scalability, adaptive trust assessment, interoperability, and energy efficiency are discussed, thereby providing directions for future researches on secure and efficient cross-domain UAV service orchestration.
Abstract The 21st century digital transformation and rapid development of blockchain technology create fundamentally new challenges for legal regulation. The increasing popularity and economic significance of cryptocurrency as a digital asset makes its legal qualification and, consequently, regulation within the framework of inheritance law relevant. The global cryptocurrency market capitalization already reaches trillions of dollars, and millions of individuals and legal entities use crypto assets as an investment instrument, payment method, and value storage mechanism. From this reality, critical legal questions arise about inheritance in cases of cryptocurrency holders’ death. The complexity of the problem is determined by the unique characteristics of cryptocurrency: decentralized nature, cryptographic protection, private key system, and high degree of anonymity create specific difficulties for heirs’ access to and identification of these assets. This research analyzes the current state of cryptocurrency inheritance legal regulation using comparative legal methods, identifies existing problems, and develops recommendations for improving legal regulation based on international experience from the USA, Germany, Japan, South Korea, and Australia.
Abstract The rapid growth of digital finance—including FinTech platforms, online payment gateways, and e-commerce marketplaces—has revolutionized global financial systems while significantly expanding the cyber-attack surface. Sophisticated attacks such as AI-generated deepfakes, automated malware, ransomware, and synthetic identity fraud now threaten financial transactions. In response, cybersecurity strategies are evolving toward decentralized models, AI-enabled detection systems, Zero Trust architectures, and quantum-safe cryptography. This paper synthesizes recent academic research and industry developments (2025–2026), covering threat taxonomies, defensive strategies, emerging attack vectors, and regulatory enhancements in payment authentication. The integration of these trends underscores the necessity of robust, AI-driven, and compliance-aware security architectures for securing modern financial ecosystems.