The pseudo-anonymity and rapidly expanding ecosystem of Decentralized Finance (DeFi) have brought about significant liquidity on EVM-compatible blockchains, making them lucrative targets for cybercriminals. In the modern financial landscape, the need for an automated, high-speed, and effective illicit money tracing system is more urgent than ever to support regulators, on-chain service providers and security practitioners in their efforts to combat the frequent and large-scale occurrences of cyber financial crimes. In this paper, we propose MFTracer, an automated system for tracing illicit money flows on EVM-compatible blockchains. Against the backdrop of a domain where tracing remains labor-intensive and expert-driven, MFTracer is developed in response to two pressing real-world demands: operational efficiency and forensic effectiveness. In response to the sophisticated fund transfer mechanisms enabled by the EVM environment, we introduce a novel fine-grained technique that enables protocol-agnostic transaction-level fund flow analysis. We further propose MFA, a lightweight and purpose-built graph abstraction with a tailored storage backend, to support efficient data retrieval. We also present a simulation algorithm for downstream illicit flow discovery. We implemented MFTracer. Its infrastructure for data retrieval achieves 3.7Ă to 9.4Ă higher storage efficiency while being 14.1Ă to 300Ă faster than the leading graph database systems. Furthermore, applied to real-world cybercrime incidents, MFTracer achieved 94.09% coverage of illicit money flows. It also newly reported 686 blockchain addresses and 4183 related transactions involved in money laundering that were previously undiscovered. MFTracer was able to reconstruct complete fund flow trajectories and provide strong evidence to investigators for 120.9 million in stolen assets.
This paper examines the Financial Accounting Standards Boardâs (FASB) recent changes to define crypto assets, focusing on why non-fungible tokens (NFTs), utility tokens and asset-backed tokens (ABTs) were not included. By examining the core features of these excluded assets, the research unpacks the reasoning behind their omission. The absence of these assets from the standard definition creates challenges for financial institutions. Without clear accounting guidance, companies face uncertainty in valuation, liquidity risk and difficulty meeting compliance requirements. Risk managers are left guessing how to assess and report these holdings. This has an impact on everything from disclosures to capital planning. The findings highlight a critical need for accounting standards that keep pace with the complexity and growth of digital assets. Institutions may misprice assets, misjudge exposure and fall short of regulatory expectations without up-to-date guidance. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
K. C. Krishnachalitha, Dikshit Sharma, Samaksh Goyal, K. Yuvaraj ¡ 6 authors
The present research combines Blockchain, 5G, and Green Computing to create secure, energy-efficient, and sustainable digital environments through a careful investigation and simulation-based analysis. Using the PRISMA approach, we choose 67 of the initial 312 papers that were related to our research. We choose these studies because they were related to our research. We created a multi-objective optimization model: f(x) = ιC(x) + βE(x) + γL(x). By using this strategy, they keep our expenses, energy use, and latency low while also keeping safety and flexibility high. We were able to simulate the mixed architecture that was demonstrated in MATLAB and NS-3 by using evidence-of-stake and PBFT consensus approaches, edge computing, and ecologic routing. The results indicate that these systems have 37% less latency, 24% more energy efficiency, and 18% less carbon emissions than systems that simply use 5G. The results show that using these three in combination helps to build solid basis for Internet of Things, medical facilities, and smart towns. This, in turn, leads to the building of facilities that are not just incredibly effective but also last for a very long period. The structure also sets the stage for Industry 5.0, that will lead to greater research in areas like quantum-proof Bitcoin, artificial intelligence-based control, and sustainability made feasible by 6G infrastructure.
Stablecoins are crypto-assets designed to maintain a stable value against a reference asset, typically the U.S. Dollar. The peg to the dollar is supported by the assets that back the stablecoin. Stablecoins perform dollar-like functions in decentralized finance (DeFi) and represent a run-able liability for their issuers.
Non-fungible tokens (NFTs) serve as a representative form of digital asset ownership and have attracted numerous investors, creators, and tech enthusiasts in recent years. However, related fraud activities, especially phishing scams, have caused significant property losses. There are many graph analysis methods to detect malicious scam incidents, but no research on the transaction patterns of the NFT scams. Therefore, to fill this gap, we are the first to systematically explore NFT phishing frauds through graph analysis, aiming to comprehensively investigate the characteristics and patterns of NFT phishing frauds on the transaction graph. During the research process, we collect transaction records, log data, and security reports related to NFT phishing incidents published on multiple platforms. After collecting, sanitizing, and unifying the data, we construct a transaction graph and analyze the distribution, transaction features, and interaction patterns of NFT phishing scams. We find that normal transactions on the blockchain accounted for 96.71% of all transactions. Although phishing-related accounts accounted for only 0.94% of the total accounts, they appeared in 8.36% of the transaction scenarios, and their interaction probability with normal accounts is significantly higher in large-scale transaction networks. Moreover, NFT phishing scammers often carry out fraud in a collective manner, targeting specific accounts, tend to interact with victims through multiple token standards, have shorter transaction cycles than normal transactions, and involve more multi-party transactions. This study reveals the core behavioral features of NFT phishing scams, providing important references for the detection and prevention of NFT phishing scams in the future.
As soon as supply chain management is done well in online marketplaces, it will lead to better logistics, lower costs, and satisfied customers. Customers are also happier. When Blockchain technology and Federated Learning (FL) are used together, they could make things safer. demand estimates that are accurate and the protection of personal information are both important. There is a lack of trust between stakeholders, data silos, and the possibility of cyberattacks, which are some of the difficulties that are associated with the supply chain management systems of today. By utilizing these models, individuals are able to make educated estimations regarding the amount of demand that will be there in the future. For the purpose of addressing these concerns, we suggest using a technique known as Blockchain-Based Decentralized Federated Learning (BC-DFL), which is a mechanism for forecasting demand. Using the immutable ledger that blockchain provides, this technology ensures the secure transportation of data. Having the ability to see demand in real time, providing members of the supply chain with increased trust, and requiring less assistance from third-party aggregators are just some of the many benefits that come with the solution that is proposed. Based on the findings, it is clear that the BC-DFL paradigm leads in an increase in demand. With five to ten nodes with fifty thousand to one hundred thousand data, the BC-DFL architecture may be able to achieve fifty rounds of proof-of-stake consensus and AES-256 encryption. The mean absolute error was 7.6, the root mean square error was 12.9, the R2 value was 0.91, and less than 5% of the data escape.
Vehicular Ad Hoc Networks (VANETs) are emerging as a cornerstone of intelligent transportation systems, enabling efficient data exchange between vehicles and roadside infrastructure. However, ensuring secure and trustworthy routing remains a major challenge due to high node mobility, intermittent connectivity, and malicious node behavior. This research proposes a Blockchain-Assisted Trust-Driven Secure Routing Protocol (BTDSRP) to enhance communication reliability, data integrity, and routing efficiency in VANETs. The proposed BTDSRP integrates blockchain technology with a hybrid trust evaluation model to ensure transparent and tamper-proof routing decisions. Each vehicle maintains both direct trusts, derived from historical packet forwarding behavior, and indirect trust, computed from neighboring recommendations. These trust values are recorded in a lightweight blockchain maintained through a Delegated Proof of Stake-Byzantine Fault Tolerant consensus mechanism, ensuring decentralized verification with minimal latency. Routing decisions are optimized using a Multi-Objective Optimization Function that jointly considers trust, link stability, delay, and residual energy to identify the most reliable path for data forwarding. A Trust Update Algorithm dynamically adjusts node reputation based on deviation from expected behavior, thereby isolating malicious nodes in real time. Extensive simulations using NS-3 and SUMO demonstrate that BTDSRP achieves superior performance compared to the conventional protocols such as AODV, DSR, and TRUSTAODV, yielding improvements of up to 18.7% in packet delivery ratio, 23.4% reduction in end-to-end delay, and 15.2% decrease in routing overhead. The results confirm that integrating blockchain with trust-based routing significantly enhances the security, transparency, and efficiency of vehicular communication networks, providing a robust foundation for next-generation 5G/6G-enabled intelligent transportation systems.
Shaista Ashraf Farooqi, Aedah Abd Rahman Rahman, Amna Saad
The growing integration of the Internet of Medical Things (IoMT) into healthcare has amplified the need for secure and privacy-preserving artificial intelligence. Federated Learning (FL) has emerged as a pivotal paradigm for decentralized medical data processing; however, it still faces challenges concerning data confidentiality, trust management, and scalability. This review presents an extended theoretical comparison of two prominent privacy-preserving frameworksâFederated Learning with Differential Privacy (FL-DP) and Federated Learning with Blockchain (FL-BC)âto assess their suitability for ensuring data security, transparency, and regulatory compliance in IoMT environments. The FL-DP framework safeguards patient data through noise injection during model updates, offering mathematically proven privacy guarantees. Conversely, the FL-BC framework reinforces trust and integrity via immutable ledgers and consensus mechanisms such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT). Reviewing literature published between 2021 and 2025, this study examines trade-offs in privacy, scalability, latency, and energy efficiency, while highlighting emerging hybrid architectures that integrate both approaches. The findings reveal that FL-DP provides stronger privacy control, whereas FL-BC ensures verifiable trust and traceabilityâtogether forming the foundation for next-generation secure and trustworthy federated learning systems in IoMT-driven healthcare.
Cryptocurrency markets are difficult to model due to high volatility and multi-scale dynamics. This study investigates the directional predictability of crypto asset prices across multiple forecast horizons using Support Vector Machines (SVM). A daily Ethereum dataset (2018-2025), comprising candlesticks, technical indicators, and sentiment data, is used to predict upward or downward price movements from one to thirty days ahead. Model interpretability is achieved through SHAP, a popular XAI methodology, which quantifies feature contributions across various horizons. Results show that short-term forecasts approach random performance, while accuracy rises steadily with horizon length, peaking near 70% around the 24-day horizon. SHAP analysis reveals that short horizons rely on fast-reacting momentum indicators, whereas longer horizons emphasize slower, trend-following features. These findings highlight that medium-term price movements contain more structured information and demonstrate how explainable machine learning can uncover horizon-dependent dynamics in digital asset markets.
Transaction fees play a crucial role in determining the efficiency and scalability of blockchain networks, particularly in Ethereum, where gas fees fluctuate significantly due to network congestion and competitive bidding. This study analyzes transaction fee patterns in the Ethereum blockchain and their impact on network efficiency by examining key blockchain metrics such as block density, transaction size, and transaction fee variability. The findings indicate that the mean transaction fee is 0.0342 ETH, with a median of 0.0008 ETH, demonstrating significant fee variability. The study also finds a strong positive correlation (r â 0.75, p < 0.01) between transaction fees and block density, as well as a moderate correlation with transaction size (r â 0.58, p < 0.01), highlighting the direct impact of network congestion on fee structures. Time series forecasting with Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models reveals cyclical trends in transaction fees, often influenced by major network activities such as NFT releases, DeFi protocol surges, and high-frequency trading. The LSTM model achieves a lower RMSE (0.09) compared to ARIMA (0.15), demonstrating its superior predictive capability for fee trends. Additionally, anomaly detection techniques identify outlier transactions with fees exceeding 2.5 ETH, often associated with front-running strategies, priority gas auctions (PGA), and inefficient smart contract executions. Despite improvements introduced by EIP-1559, the findings indicate that Ethereumâs transaction fee market remains highly volatile, with block density fluctuating between 512.0% and 3896.0%, causing extreme fee spikes during congestion periods. The presence of large transactions (maximum size: 250 bytes) further amplifies fee inefficiencies, reinforcing the need for improved scalability solutions. This study underscores the necessity of Layer-2 rollups, dynamic block size adjustments, and more adaptive fee mechanisms to enhance blockchain efficiency. Future research should explore comparative studies across blockchain networks, advanced predictive modeling techniques, and the role of miner extractable value (MEV) in transaction ordering fairness. The studyâs insights provide valuable guidance for developers, users, and policymakers aiming to optimize Ethereumâs transaction fee structure and enhance overall blockchain performance.
In today’s technologically advancing world, many fields from finance to healthcare and education are shifting toward a digital and decentralized format. A significant transformation is underway with the currency of the masses. Blockchain-based cryptocurrencies like Bitcoin and Ethereum allow users to generate fungible tokens anonymously through smart contracts. However, these features also facilitate illicit transactions and cybercrimes like fraud, phishing, and money laundering. The proposed work explores the identification of suspicious transactions on the Ethereum blockchain by leveraging advanced machine-learning techniques. An Extreme Gradient Boosting (XGBoost) classifier is optimized for spotting unauthorized or malicious transactions, exploring features like transaction patterns and value anomalies. Feature scaling and log transformations normalize skewed distributions, while rigorous model training and hyperparameter tuning enhance the system's precision, recall, and overall accuracy. Other aids, such as feature importance rankings, precision-recall curves, and diagnostic statistics, provide useful information on fraud patterns. Evaluation of the model shows that integrating cost-sensitive learning significantly reduces false positives, from 51 to 44, representing a 13.7% decrease, which enhances practical usability by minimizing false alerts and manual verification efforts. Although there was a slight increase in false negatives (from 14 to 15), the overall classification accuracy improved. The model demonstrated strong performance in managing class imbalance which is common in fraud detection contexts.
Varshan Manish, M Gokulesh, P A Dharshan Raj, K Ragavan
The blockchain based system and method for multi-form gold tokenization by using ERC-1155 token standard focuses on a single core idea of using ERC-1155 semi fungible tokens to enable scalable and trustworthy digital gold markets that simultaneously serve institutional reserve holders and retail fractional owners. This research presents the ERC-1155 token model tailored for gold assets semantics defined conversion rules between reserve classes and fractional units and describing contract interfaces required for custody aware issuance and secure redemption and also by providing a reference implementation (smart contract + client DApp). The implementation on public Ethereum test nets is evaluated to measure operational characteristics of the ERC-1155 token standard. The study also discusses governance and custody considerations necessary to make ERC-1155 based gold tokenization practical. Results show that a single ERC-1155 contract can express both fungible fractions and non-fungible reserve classes while being computationally inexpensive and is 13.6% more relatively efficient when it comes to burning operation while it is 0.63% more relatively efficient when it comes to minting operation when compared with the same operations that makes use of ERC-721 token standard.
Neide Judith Faria de Oliveira, Francisco Carneiro da Silva Filho
A pesquisa discute o uso da tecnologia NFT (Non-Fungible Tokens) no mercado de arte, considerando a exclusividade dessas obras. As NFTs permitem exibição em galerias virtuais no Metaverso, associadas ao blockchain, tanto como ativos fĂsicos quanto intangĂveis (propriedade intelectual). A tecnologia reforça noçþes de herança e propriedade, mas enfrenta desafios como altos custos, falta de regulamentação, consumo de energia e direitos autorais. A pesquisa explora as potencialidades dessa tecnologia e a necessidade de procedimentos claros para garantir transaçþes justas e transparentes no mercado artĂstico.
Ethereum, as a leading blockchain platform, experiences high variability in transaction fees due to network congestion, gas bidding, and computational complexity. This study analyzes 10,000 Ethereum transactions to identify key factors influencing transaction fees, block density, and staking mechanisms. The results show that transaction fees vary significantly, with an average of 0.1826 ETH and a standard deviation of 0.2381 ETH, indicating substantial fluctuations. A strong positive correlation (r = 0.72) between transaction size and transaction fee confirms that larger transactions incur higher costs due to increased computational demand. Time-series analysis reveals periodic spikes in gas fees, aligning with network congestion patterns. Block density averages 1718.8% (std = 501.01%), showing that some blocks are highly congested while others are underutilized. An Isolation Forest anomaly detection model identifies 3.4% of transactions as outliers, exhibiting unusually high gas fees, which may be caused by priority-based bidding, inefficient smart contract execution, or potential fee manipulation. Further analysis demonstrates that Coin Age and Stake Reward significantly influence transaction success rates. Transactions with older coins show a 7.8% higher success rate, indicating that validators may prioritize transactions with greater historical weight. Additionally, Stake Reward positively affects the Block Generation Rate (p < 0.05), confirming its role in securing the network and optimizing transaction processing. These findings provide valuable insights for Ethereum users, developers, and validators to optimize gas fees, transaction timing, and staking incentives. While this study offers critical observations, future research should focus on real-time gas fee monitoring, deep learning-based congestion forecasting, and the impact of Layer-2 scaling solutions. Understanding Ethereumâs Proof-of-Stake (PoS) dynamics will be essential for ensuring fair transaction processing, reducing gas fees, and improving blockchain efficiency.
This study examines the relationship between gas prices and transaction values on the Ethereum blockchain, providing a detailed analysis of transaction dynamics and the factors influencing gas price determination. The correlation coefficient between gas prices and transaction values is -0.0273, indicating a very weak and negative relationship. Instead, gas prices are driven by factors such as computational intensity, network congestion, and user prioritization. Functions with higher computational demands, such as mint, recorded the highest mean gas price of 120.45 Gwei, with a standard deviation of 15.30 Gwei, while functions like approve and transfer exhibited mean gas prices of 98.30 Gwei and 110.80 Gwei, respectively. Recipient address analysis reveals a strong concentration of transaction values, with the top recipient address receiving 49.95 ETH consistently, indicating high-value operations directed toward specific accounts. High-gas transactions, defined as those above the 90th percentile, displayed a mean gas price of 191.96 Gwei with minimal variability, while their corresponding transaction values varied widely, with a mean of 23.91 ETH and a standard deviation of 13.66 ETH. These findings provide critical insights into Ethereum transaction behavior, emphasizing the role of function type and user prioritization in shaping gas price decisions. Future research should investigate the impact of network upgrades such as EIP-1559, the adoption of Layer-2 scaling solutions, and temporal trends in transaction behavior to enhance network scalability and cost efficiency as Ethereum continues to evolve.
Amit Sharma, K A Balaji, Jitha Janardhanan, Ranganathaswamy Madihalli Kenchappa ¡ 6 authors
With the rapid expansion of healthcare data, especially Electronic Health Records (EHRs), there are significant concerns about data security, privacy, interoperability, and accessibility. When utilizing conventional centralized EHR systems, patients usually lack transparency and control over their medical data. In order to handle data safely, this study proposes the B-DECIDE (Blockchain-Driven Electronic Health Record Control, Integrity, and Data Efficiency framework), a decision-making paradigm that leverages the immutability, decentralization, and cryptography aspects of blockchain technology. B-DECIDE evaluates a number of blockchain topologies, including as public, private, and consortium, together with consensus algorithms like Proof of Work (PoW) and Proof of Stake (PoS), in order to determine how effective healthcare is. The findings indicate that a hybrid on-chain/off-chain storage approach maintains scalability, enhances data integrity, reduces latency, and ensures regulatory compliance.
Aleksei Olkhovikov, Yash Madhwal, Arsen Andrian, Hamza Imran ¡ 8 authors
⢠Prototype system with Raspberry Pi and dual ultrasonic sensors for data acquisition. ⢠Real-time data signing and blockchain submission using web3.py and EVM chain. ⢠Smart contract for secure data logging, access control, and gas-efficient events. ⢠Frontend with Streamlit MVP and Vue3 dashboard supporting secure user login. ⢠Experiments on 15M-record dataset to evaluate gas cost, batching, and scalability. Integrity and traceability of sensor data in oilfield operations are essential for safe, efficient, and compliant resource extraction. This paper presents a blockchain-enabled proof-of-concept (PoC) IoT framework that facilitates decentralized, tamper-evident monitoring of oil extraction infrastructure. The system integrates field-deployed sensors with a Raspberry Pi-based edge controller to capture, buffer, and cryptographically sign telemetry data, which is then submitted to an EVM-compatible blockchain using smart contracts. The PoC demonstrates historical and real-time data visualization through a web-based dashboard that authenticates and displays blockchain event streams. A real-world drilling data set comprising more than 15 million records is used for the experimental evaluation of the prototype. Gas consumption metrics are analyzed under varying payload sizes and batching strategies, revealing linear scalability with respect to parameter volume and significant efficiency gains through transaction batching. These results demonstrate measurable improvements in resource utilization and operational cost, confirming the frameworkâs efficiency and robustness for large-scale industrial telemetry. The architecture supports secure access control, structured metadata annotation, and transparent logging without reliance on centralized intermediaries. By addressing key challenges in data authenticity and operational visibility, the proposed solution establishes a scalable foundation for secure telemetry in oil and gas operations, with potential applicability to other critical infrastructure domains such as energy grids, mining, and water resource management. Unlike prior blockchain-IoT frameworks focusing primarily on architectural design or off-chain coordination, the proposed system demonstrates an end-to-end implementation directly linking field-level sensors to on-chain storage and visualization. Through large-scale validation on a 15 M-record drilling dataset, this work provides one of the first empirical analyses of gas-efficient, real-time telemetry submission in industrial settings.
Michele Pasqua, Sofia Mari, Ferdinando Santoro, Mariano Ceccato
With Ethereumâs rise as the leading platform for decentralized applications, securing Ethereum smart contracts, very often having a financial impact, becomes paramount. Existing research lacks a comprehensive overview of Ethereum defects (and the terminology is often inconsistent), making it difficult for researchers, developers, and industry professionals to navigate this nowadays critical topic. This necessitates a unified source of information detailing defects affecting Ethereum and its smart contracts, along with their root causes, impact, and mitigation strategies. In this paper, we propose a knowledge base of defects , encompassing security vulnerabilities and code flaws found in the Ethereum blockchain and its smart contracts. We started by performing a systematic literature review to identify the currently known defects and then created a hierarchical tag system to classify them. This system was then used to build an ontology allowing users to easily search and learn about Ethereum defects. We also implemented EDOV, a tool to graphically navigate and explore the ontology, perform search queries, and visualize defect details, such as examples of defective/fixed code. As new defects may appear in the future, the ontology and the tool are built with extensibility in mind. We believe this research is a valuable contribution to helping developers and practitioners avoid common mistakes, improving the overall security and reliability of the Ethereum ecosystem.
Zero-knowledge proofs (ZKPs) enable a prover to convince a verifier of knowledge of a secret without revealing it. The ZKP for the square-root problem has many applications in network and cloud security, such as user authentication and privacy-preserving cloud storage auditing. Classical protocols for the quadratic residuosity (square-root) relation require multiple iterations to reach negligible soundness error, incurring latency and communication costs that are critical in cloud settings. This paper proposes a new single-round zero-knowledge proof (SR-ZKP) for the square-root problem that achieves the same soundness as iterative schemes by increasing the challenge length. The protocol requires only one execution of a 4-message protocol (request, commit, challenge, response) and can be transformed into a one-message non-interactive ZKP via the FiatâShamir heuristic. The completeness, soundness, and zero-knowledge properties of the proposed scheme are formally proven. The results of this study show that the proposed protocol can achieve approximately \(97\%\) reduction in communication overhead and latency, when compared to an 80-round iterative ZKPs with RSA modulus n of size 2048 bits. This provides a substantial advantage for cloud applications.
Virtual Power Plant (VPP) trading mechanisms confront unprecedented challenges from behavioral complexities and technological uncertainties that conventional rational choice models inadequately address. This research develops an integrated framework combining prospect theory-driven decision modeling with evolutionary smart contracts and multi-stage negotiation protocols to enhance trading effectiveness in cross-regional energy markets. We establish mathematical foundations incorporating loss aversion, probability distortion, and reference-dependent preferences into VPP decision-making, while developing adaptive contracts capable of autonomous evolution responding to market changes. Through composite game-theoretic analysis examining nested interactions between contract evolution and negotiation dynamics, we validate the framework across three comprehensive scenarios: emergency dispatch under extreme weather, renewable energy integration, and cross-regional collaboration. Simulation results demonstrate 15â25% negotiation efficiency improvements compared to traditional mechanisms, with behavioral models capturing significant heterogeneity in loss aversion coefficients (2.1â3.4) across VPP configurations. The evolutionary contracts successfully adapt within 72-hour windows to policy changes and technological developments, while maintaining system stability. Cross-regional analysis reveals how cultural distance and information asymmetries influence trading outcomes, with the framework achieving superior market integration despite these barriers. These findings establish new paradigms for behaviorally-informed energy market design, offering transformative implications for renewable integration and decentralized electricity systems.