André Augusto, André Vasconcelos, Miguel Correia, Luyao Zhang
The number of blockchain interoperability protocols for transferring data and assets between blockchains has grown significantly. However, no open dataset of cross-chain transactions exists to study interoperability protocols in operation. There is also no tool to generate such datasets and make them available to the community. This paper proposes XChainDataGen, a tool to extract cross-chain data from blockchains and generate datasets of cross-chain transactions (cctxs). Using XChainDataGen, we extracted over 35 GB of data from five cross-chain protocols deployed on 11 blockchains in the last seven months of 2024, identifying 11,285,753 cctxs that moved over 28 billion USD in cross-chain token transfers. Using the data collected, we compare protocols and provide insights into their security, cost, and performance trade-offs. As examples, we highlight differences between protocols that require full finality on the source blockchain and those that only demand soft finality (\textit{security}). We compare user costs, fee models, and the impact of variables such as the Ethereum gas price on protocol fees (\textit{cost}). Finally, we produce the first analysis of the implications of EIP-7683 for cross-chain intents, which are increasingly popular and greatly improve the speed with which cctxs are processed (\textit{performance}), thereby enhancing the user experience. The availability of XChainDataGen and this dataset allows various analyses, including trends in cross-chain activity, security assessments of interoperability protocols, and financial research on decentralized finance (DeFi) protocols.
Atomic swaps have been widely considered to be an ideal solution for cross-chain cryptocurrency transactions due to their trustless and decentralized nature. However, their adoption in practice has been strictly limited compared to centralized exchange order books because of long transaction times (anywhere from 20 to 60 minutes) prohibiting market makers from accurately pricing atomic swap spreads. For the decentralized finance ecosystem to expand and benefit all users, this would require accommodating market makers and high-frequency traders to reduce spreads and dramatically boost liquidity. This white paper will introduce a protocol for atomic swaps that eliminates the need for an intermediary currency or centralized trusted third party, reducing transaction times between Bitcoin and Ethereum swaps to approximately 15 seconds for a market maker, and could be reduced further with future Layer 2 solutions.
Huan Margana Ritonga, Zara Yunizar, Hafizh Al Kautsar Aidilof
Cryptocurrency has become one of the most intriguing topics in finance and technology in recent years. With the growing prominence of Bitcoin, the rise of altcoins (alternative cryptocurrencies) also demonstrates significant potential within the cryptocurrency market. Altcoins, which include all cryptocurrencies other than Bitcoin, offer diverse functionalities and use cases, ranging from smart contracts to decentralized finance (DeFi) applications. This thesis identifies the altcoin options with the best investment opportunities and the highest growth potential. The study employs the Preference Selection Index (PSI) method, a multi-criteria decision-making approach that evaluates alternatives based on specific preferences and criteria. This method is particularly suitable for assessing complex investment decisions involving multiple variables, such as market capitalization, technological innovation, and utility. By applying PSI, investors can decide which altcoins will likely yield substantial returns. A web-based platform has been developed as part of this research to simplify selecting promising altcoins. This platform enables users to evaluate options based on predefined criteria, such as market trends, project objectives, and development team credibility. The accessibility of this tool empowers users—whether novice or experienced investors—to navigate the dynamic cryptocurrency market more effectively. Altcoins provide a unique opportunity for diversification in investment portfolios. Unlike Bitcoin, which is often viewed as a store of value, many altcoins are designed with specific purposes and innovative features. For instance, Ethereum introduced smart contracts that revolutionized decentralized applications, while other altcoins focus on scalability or niche markets like the Internet of Things (IoT). However, investing in altcoins also comes with challenges like high market volatility, security risks, and regulatory uncertainties. Therefore, thorough research and strategic planning are essential for minimizing risks while maximizing returns in this rapidly evolving sector.
The integration of Internet of Things (IoT) devices in healthcare has enhanced medical efficiency but poses challenges such as data privacy risks and internal abuse. Traditional IoT data access frameworks suffer from centralization, limited scalability, and static permission controls. To address these issues, we propose MedAccessX, a blockchain-based access control framework combining attribute-based access control (ABAC) and role-based access control (RBAC). MedAccessX utilizes four types of smart contracts: a user management contract (UMC) for managing user operations, a medical data management contract (MDMC) for handling data, a policy contract (PC) for managing access rights, and an access control contract (ACC) for enforcing permissions and facilitating data sharing. Our evaluation, conducted on a private Ethereum blockchain network with multiple nodes, assesses security, deployment cost, gas consumption, throughput, and response time. Comparative analysis demonstrates that MedAccessX achieves lower deployment costs and higher throughput, outperforming existing solutions.
Detecting fraudulent activities such as Ponzi schemes within smart contract transactions is a critical challenge in decentralized finance. Existing methods often fail to capture the heterogeneous, multi-faceted nature of blockchain data, and many graph-based models overlook the contextual patterns that are vital for effective anomaly detection. In this paper, we propose MVCG-SPS, a Multi-View Contrastive Graph Neural Network designed to address these limitations. Our approach incorporates three key innovations: (1) Meta-Path-Based View Construction, which constructs multiple views of the data using meta-paths to capture different semantic relationships; (2) Reinforcement-Learning-Driven Multi-View Aggregation, which adaptively combines features from multiple views by optimizing aggregation weights through reinforcement learning; and (3) Multi-Scale Contrastive Learning, which aligns embeddings both within and across views to enhance representation robustness and improve anomaly detection performance. By leveraging a multi-view strategy, MVCG-SPS effectively integrates diverse perspectives to detect complex fraudulent behaviors in blockchain ecosystems. Extensive experiments on real-world Ethereum datasets demonstrated that MVCG-SPS consistently outperformed state-of-the-art baselines across multiple metrics, including F1 Score, AUPRC, and Rec@K. Our work provides a new direction for multi-view graph-based anomaly detection and offers valuable insights for improving security in decentralized financial systems.
ABSTRACT This study examines how task assignment mechanisms affect the participation of workers on decentralized blockchains. In developing the theory, I highlight that blockchain represents a distinct organizational form for coordinating operations under a highly decentralized structure, in which the essential tasks of system infrastructure maintenance are assigned to third‐party crowd workers through the unique governance mechanism of consensus protocol. I specifically focus on two widely adopted consensus protocols in the context of cryptocurrency, namely, proof‐of‐work (PoW), which assigns tasks that sustain the blockchain system operation based on workers' investments in computing power, and proof‐of‐stake (PoS), which assigns these tasks based on workers' investments in the native cryptocurrency as stakes. I argue that compared with PoW, PoS increases worker participation and task decentralization because the investment requirement of task participation in the form of blockchain native assets reduces workers' transaction costs in task contracting and their tendencies for hyper‐competition. My empirical analysis leverages a natural experiment on Ethereum, namely, the “Merge” event on September 15, 2022, in which the blockchain changed the assignment rules by switching the consensus protocol from PoW to PoS. The results under a difference‐in‐differences research design confirm my arguments.
Cryptocurrency represents a form of asset that has arisen from the progress of financial technology, presenting significant prospects for scholarly investigations. The ability to anticipate cryptocurrency prices with extreme accuracy is very desirable to researchers and investors. However, time-series data presents significant challenges due to the nonlinear nature of the cryptocurrency market, complicating precise price predictions. Several studies have explored cryptocurrency price prediction using various deep learning (DL) algorithms. Three leading cryptocurrencies, determined by market capitalization, Ethereum (ETH), Bitcoin (BTC), and Litecoin (LTC), are examined for exchange rate predictions in this study. Two categories of recurrent neural networks (RNNs), specifically long short-term memory (LSTM) and gated recurrent unit (GRU), are employed. Four performance metrics are selected to evaluate the prediction accuracy namely mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) for three cryptocurrencies which demonstrates that GRU model outperforms LSTM. The GRU model was implemented as a two-layer deep learning network, optimized using the Adam optimizer with a dropout rate of 0.2 to prevent overfitting. The model was trained using normalized historical price data sourced from CryptoDataDownload, with an 80:20 train-test split. In this work, GRU qualifies as the best algorithm for developing a cryptocurrency price prediction model. MAPE values for BTC, LTC and ETH are 0.03540, 0.08703 and 0.04415, respectively, which indicate that GRU offers the most accurate forecasts as compared to LSTM. These prediction models are valuable for traders and investors, offering accurate cryptocurrency price predictions. Future studies should also consider additional variables, such as social media trends and trade volumes that may impact cryptocurrency pricing.
Daria Smuseva, Andrea Marin, Sabina Rossi, Aad van Moorsel
A blockchain is an immutable ledger driven by a distributed consensus protocol. In public blockchains, such as Bitcoin and Ethereum Classic, consensus is established through a computational effort called Proof-of-Work (PoW). Special users called miners contribute to the PoW in exchange for a fee and also verify the data stored in blocks mined by the other miners. Here is where the Verifier’s Dilemma emerges. Verification of blocks does not receive a reward, and to maximise their profits, miners may be incentivised to forego verifying blocks and to only invest their resources in PoW. In this article, we study the Verifier’s Dilemma and a possible countermeasure consisting of the injection of invalid blocks using a quantitative model based on Markovian process algebra. To avoid the state space explosion problem, we study the underlying Markov chain by using a lumping that allows us to derive closed-form solutions for interesting performance indices. The analysis demonstrates the circumstances under which non-verifying miners gain fees higher than those of verifying miners. The model also allows us to derive the optimal rate at which invalid blocks must be injected so that skipping the verifying phase becomes economically disadvantageous whereas the throughput of the blockchain is only minimally reduced. The impact on miners’ rewards and overall performance is also assessed.
This study discusses phishing detection on the Ethereum network using machine learning methods, specifically Graph Convolutional Networks (GCNs) and Enhanced Graph Attention Networks (EGAT). The background of this research is based on the increasing number of phishing attacks in the blockchain ecosystem that can threaten the financial security of users. The research aims to analyze the incidence rate of phishing attacks and develop effective and efficient detection methods. The methodology includes data collection from Ethereum transactions and phishing activities, followed by feature extraction, machine learning model training, and evaluation using metrics such as accuracy, precision, recall, and F-score. The identified research gap is the lack of focus on early-stage phishing detection in the Ethereum network and the suboptimal performance of existing methods in recognizing complex transaction patterns. The results indicate that EGAT achieves an accuracy of 93.6%, outperforming GCNs, which reach 91.2%. The conclusion of this research is that the EGAT method is superior in detecting phishing activities, providing significant contributions to security in the Ethereum network.
Blockchain technology is transforming industries like finance, supply chain, governance, and healthcare. This paper analyzes blockchain architecture, applications, challenges, and performance. Key attributes—decentralization, immutability, transparency, and security—enable secure peer-to-peer transactions without intermediaries. Consensus mechanisms such as Proof of Work (PoW) and Proof of Stake (PoS) are examined for their trade-offs in scalability, energy efficiency, and security. In healthcare, blockchain addresses security and interoperability issues in centralized Personal Health Record (PHR) systems. Solutions using Ethereum, Hyperledger, smart contracts, and IPFS enhance Electronic Health Record (EHR) management by improving data integrity, privacy, and access control while reducing costs. A containerized microservices architecture further enhances scalability. Blockchain performance, evaluated using the BLOCKBENCH framework, highlights gaps in transaction throughput compared to traditional databases. Despite scalability, interoperability, and regulatory challenges, ongoing research focuses on optimizing consensus mechanisms, integrating database principles, and improving healthcare interoperability, advancing blockchain’s real-world applications
Decentralized balloting using Ethereum blockchain is a cozy, obvious and tamper-proof manner of undertaking on line voting.Most existing E-Voting systems are based on centralized servers where the voters must trust the organizing authority for the integrity of the results. it’s far a decentralized utility built on the Ethereum blockchain network, which allows contributors to solid their votes and look at the balloting effects with out the need for intermediaries.Blockchain is an immutable and indisputable public ledger. These ledgers exist in different locations, so any single failure does not affect the distributed ledger. In this gadget, votes are recorded on the blockchain, making it impossible for all people to control or regulate the outcomes. the usage of smart contracts guarantees that the vote casting technique is computerized, transparent, and comfortable. the usage of the blockchain generation and the implementation of a decentralized device provide a dependable and cost-powerful solution for undertaking honest and truth- ful elections.One essential democratic action is voting. Paper balloting, according to many experts, is the only suitable way to guarantee everyone’s right to vote. However, this approach is prone to misuse and mistakes. To overcome the challenges associated with paper voting, many countries use digital voting techniques. Massive vote-rigging could result from a single digital voting defect. Voting procedures for elections must be accurate, safe, convenient, and lawful. However, acceptability might be limited by problems with digital voting techniques. To solve these issues, blockchain technology was created because of its end-to-end verification capabilities. To ensure Blockchain technology has been utilized for voting in order to provide anonymity, privacy, verifiability, mobility, integrity, security, and fairness. Our suggested approach guarantees integrity, security, and anonymity by utilizing blockchain technology. This study also examines the difficulties blockchain electronic voting systems encounter and pinpoints areas that require further investigation to improve their reliability. Index Terms—Blockchain, Ethereum, Smart contracts, E- voting, Solidity, government, industry, security, survey, trans- parency.
Identifying reputable Ethereum projects remains a critical challenge within the expanding blockchain ecosystem. The ability to distinguish between legitimate initiatives and potentially fraudulent schemes is non-trivial. This work presents a systematic approach that integrates multiple data sources with advanced analytics to evaluate credibility, transparency, and overall trustworthiness. The methodology applies machine learning techniques to analyse transaction histories on the Ethereum blockchain. The study classifies accounts based on a dataset comprising 2,179 entities linked to illicit activities and 3,977 associated with reputable projects. Using the LightGBM algorithm, the approach achieves an average accuracy of 0.984 and an average AUC of 0.999, validated through 10-fold cross-validation. Key influential factors include time differences between transactions and received_tnx. The proposed methodology provides a robust mechanism for identifying reputable Ethereum projects, fostering a more secure and transparent investment environment. By equipping stakeholders with data-driven insights, this research enables more informed decision-making, risk mitigation, and the promotion of legitimate blockchain initiatives. Furthermore, it lays the foundation for future advancements in trust assessment methodologies, contributing to the continued development and maturity of the Ethereum ecosystem.
Pierpaolo Della Monica, Ivan Visconti, Andrea Vitaletti, Marco Zecchini
Before a fair exchange takes place, there is typically an advertisement phase with the goal of increasing the appeal of possessing a digital asset while keeping it sufficiently hidden. Advertisement phases are implicit in mainstream definitions, and therefore are not explicitly integrated within fair-exchange protocols. In this work we give an explicit definition for such a fair exchange in a setting where parties communicate via broadcast messages only (i.e., no point-to-point connection between seller and buyer is needed). Next, we construct a fair-exchange protocol satisfying our new definition using zk-SNARKs and relying on mainstream decentralized platforms (i.e., a blockchain with smart contracts like Ethereum and a decentralized storage system like IPFS). Experimental results confirm the practical relevance of our decentralized approach, paving the road towards building decentralized marketplaces where users can, even anonymously, and without direct off-chain communications, effectively advertise and exchange their digital assets as part of a system of enhanced NFTs.
The increasing adoption of blockchain technology has led to a surge in financial fraud, including money laundering, Ponzi schemes, and illicit fund transfers. Traditional fraud detection techniques, such as rule-based systems and supervised machine learning models, struggle to handle the high-volume, high-velocity, and dynamically evolving nature of blockchain transactions. These limitations necessitate a scalable and adaptive approach to detect fraudulent activities efficiently. This study introduces a Spatial-Temporal Graph Neural Network (STGNN)-based fraud detection framework, specifically designed for scalable anomaly detection in large-scale blockchain networks. By modeling blockchain transactions as a spatial-temporal graph, the proposed system captures structural dependencies between wallets and temporal patterns of fund movements. The STGNN model employs graph convolutional networks (GCN) or graph attention networks (GAT) for spatial feature extraction and gated recurrent units (GRU) or temporal convolutional networks (TCN) for sequential fraud pattern recognition. Additionally, to ensure scalability, the framework incorporates graph partitioning techniques, parallelized mini-batch training, and distributed processing, enabling real-time fraud detection across high-throughput blockchain networks. Extensive experiments conducted on Bitcoin and Ethereum transaction datasets demonstrate that the STGNN model achieves higher accuracy, lower false positive rates, and improved computational efficiency compared to rule-based fraud detection systems, supervised ML models, and static GNNs. Case studies further confirm the model’s effectiveness in detecting large-scale fraud schemes, such as DeFi exploits, cross-chain laundering, and coordinated illicit transactions. This research highlights the potential of graph-based deep learning techniques in blockchain security, providing a foundation for future advancements in scalable fraud detection, cross-chain anomaly detection, and decentralized financial security monitoring.
This study investigates the return propagation dynamics between cryptocurrencies and Emerging market sectoral indices (EMSI), focusing on portfolio impact from Bitcoin, Ethereum, and two gold-backed cryptocurrencies (PAXG and X8X). Using data from 2019 to 2024, we apply a novel DCC-GARCH-based R 2 decomposed connectedness approach to analyse return connectedness among these high-risk assets. We also utilize innovative concepts such as minimum dynamic pairwise connectedness and minimum R 2 decomposed connectedness portfolios in our multivariate hedging portfolios. Our findings reveal that total connectedness is time-variant and influenced by economic events. Bitcoin and Ethereum are identified as net transmitters of shocks, while other assets, particularly gold-backed cryptocurrencies, serve as net shock receivers with minimal impact. Moreover, few EMSIs (financials, industrials, and materials sectors) show significant connectedness in the system. Although our suggested portfolio analysis offers improved returns, none consistently outperform the market. This research offers valuable insights for investors and policymakers regarding the interconnectedness and risk management of cryptocurrencies and EMSI.
The advancement of e-health systems has resulted in substantial enhancements in healthcare delivery via effective data management and accessibility. The use of digital health solutions presents dangers to sensitive health information, including unauthorised access, privacy violations, and security weaknesses. This research presents a blockchain-based paradigm for privacy-preserving authentication and access control specifically designed for e-health systems. The architecture utilises the Ethereum blockchain, smart contracts, blind signatures, Proof of Authority (PoA) consensus, and one-way hash functions to improve data integrity, security, and privacy in a decentralised framework. The proposed methodology addresses computational efficiency and scalability issues via the implementation of lightweight cryptographic techniques, achieving an average authentication delay of 0.059 milliseconds, which represents a 4000-fold improvement compared to current approaches. The model exhibits a significant decrease in memory use, requiring just 0.0198 MB in contrast to the 96.98 MB required by benchmark models, and attains an average signature verification duration of 0.00092 milliseconds. The findings demonstrate the model’s capability for safe, efficient, and scalable applications in e-health, which guarantees privacy and adherence to regulatory norms.
B. N. Das, Babul Chandra Sarker, Amit Saha, Kanchon Kumar Bishnu · 9 authors
The exponential growth of cryptocurrency implementation in the USA has brought with it a surge in correlated risks, particularly in the form of scams that exploit the relative novelty and complexity of digital currencies. The primary objective of this study was to develop machine algorithms for identifying fraud trends in cryptocurrency transactions. By employing complex analysis, this research project attempted to identify certain trends and behaviors that fall under a variety of scams, providing a platform for effective detection and counter-strategies. This study will have a definite objective in terms of Bitcoin, Ethereum, and other high-profile cryptocurrencies in America when it comes to scam analysis. The scam-related transaction dataset comprised in-depth information regarding suspicious fraud activity in the cryptocurrency environment, such as a specific ID for a transaction, timestamps, values for transactions, and labels distinguishing between suspicious and legitimate activity. A variety of proven models were selected such as Logistic Regression, Random as well Multinomial Naive Bayes, where each model had its respective weaknesses and strengths. The Random Forest algorithm attained the highest accuracy, nearing perfection which underscores its robustness and reliability in classifying both legitimate and fraudulent reports. To effectively counter fraud in cryptocurrencies, U.S. policies must be strengthened with a merger of machine intelligence in them. Regulatory agencies have to work towards developing a system that encourages exchanges to utilize complex analysis for fraud detection, perhaps in terms of reduced compliance burden for entities with effective anti-fraud controls in position. Leveraging AI insights can go a long way in supporting investigations into scams in cryptocurrencies conducted by governments. By utilizing machine algorithms trained with datasets of past scams, governments can monitor and follow illicit fund flows through the blockchain with ease.
In recent years, the Ethereum platform has witnessed a proliferation of smart contracts, accompanied by exponential growth in total value locked (TVL). High-TVL smart contracts often require complex numerical computations, particularly in mathematical financial models used by many decentralized applications (DApps). Improper calculations can introduce numerical defects, posing potential security risks. Existing research primarily focuses on traditional numerical defects like integer overflow, and there is currently a lack of systematic research and effective detection methods targeting new types of numerical defects. In this paper, we identify five new types of numerical defects through the analysis of 1,199 audit reports by utilizing the open card method. Each defect is defined and illustrated with a code example to highlight its features and potential consequences. We also propose NumScout, a symbolic execution-based tool designed to detect these five defects. Specifically, the tool combines information from source code and bytecode, analyzing key operations such as comparisons and transfers, to effectively locate defects and report them based on predefined detection patterns. Furthermore, NumScout uses a large language model (LLM) to prune functions which are unrelated to numerical operations. This step allows symbolic execution to quickly enter the target function and improve runtime speed by 28.4%. We run NumScout on 6,617 real-world contracts and evaluated its performance based on manually labeled results. We find that 1,774 contracts contained at least one of the five defects, and the tool achieved an overall precision of 89.7%.
Smart contracts on blockchain networks autonomously execute applications based on predefined conditions, making their security-critical due to the potential for significant financial losses from vulnerabilities. Current vulnerability detection algorithms commonly rely on expert-defined rules, which are prone to errors and insufficient for identifying complex vulnerability patterns . Given the immutability of smart contracts post-deployment, ensuring security before deployment is essential. This research presents Block-wise Abstract Syntax Tree based Federated Graph Neural Networks (BAST-FeGNN), a novel approach combining block-wise abstract syntax tree and Federated Graph Neural Networks (FeGNN) to detect code clones and multiclass vulnerabilities in Ethereum smart contracts. The BAST-FeGNN method operates in three stages: it first extracts security-related patterns from the base code using an abstract syntax tree; then, it constructs and normalizes a contract graph using FeGNN to capture critical nodes, analyze data and control flows. This integration of graph-based feature extraction with pattern matching allows precise detection of vulnerabilities like access control issues, reentrancy, and unchecked calls, as well as identifying code clones. Finally, the method pools these features for comprehensive vulnerability detection . BAST-FeGNN significantly enhances vulnerability detection accuracy and scalability, outperforming existing models with an accuracy of 95.35%, recall of 95.58%, F1-score of 95.80%, and precision of 96.10%, making it a robust solution for securing blockchain applications.
Decentralized smart contracts enable trustless collaboration but suffer from limited privacy and scalability, which hinders broader adoption. Trusted Execution Environment (TEE) based off-chain execution frameworks offer a promising solution to both issues. Although TEE-based frameworks have made significant progress, prior work has yet to fully explore contract interoperability, a critical foundation for building complex real-world decentralized applications. This paper identifies the key challenges impeding such interoperability and presents practical solutions. Based on these insights, we introduce RaceTEE, a novel framework that leverages off-chain TEE-enabled nodes to efficiently execute confidential, long-lived smart contracts with interactions of arbitrary complexity among contracts. We implement a RaceTEE prototype using Intel SGX, integrate it with Ethereum, and release it as open source. Evaluation across diverse use cases demonstrates its practicality and effectiveness.
The Zero Trust (ZT) model is pivotal in enhancing the security of distributed systems by emphasizing rigorous identity verification, granular access control (AC), and continuous monitoring. To address the complexity and scalability challenges of modern distributed systems, we propose a blockchain-based dynamic access control scheme (DACS) as a practical solution for implementing ZT principles. This framework dynamically manages access control lists (ACLs) and enforces policies through smart contracts. In the DACS framework, each blockchain node maintains an object list specifying access permissions within its ACL and incorporates a minimum trust metric (TM) threshold to evaluate access requests. The TM assigned to each node reflects its trustworthiness. To further enhance security, the framework includes security awareness, enabling the dynamic assessment of the risk factor (RF), which reflects the operational risk level. The TM of access-requesting nodes is updated at runtime based on their behavior, with penalties imposed for malicious actions according to the prevailing RF. Access control policies are dynamically adjusted, mitigating risks posed by potentially untrustworthy users with valid credentials. Implemented and tested on the Ethereum blockchain, the proposed DACS framework demonstrates its efficiency and effectiveness in securing distributed systems.
The integration of blockchain with Information-Centric Networking (ICN) enhances content distribution efficiency in areas such as the Internet of Things (IoT) and 5G/6G communications. This integration implies that the network state information of ICN can significantly impact consensus efficiency. However, the Ethereum Casper FFG consensus algorithm overlooks the network heterogeneity among consensus nodes, leading to a potential bottleneck in consensus efficiency, especially when nodes with inferior network quality participate. To address this issue, this paper proposes a multidimensional reputation model based on an ICN-enabled blockchain architecture. The model combines on-chain stake and network contributions to evaluate the reputation of ICN consensus nodes. Furthermore, a reputation-based hybrid consensus mechanism, RepuICN, is introduced, which enhances the network layer of the Casper FFG algorithm. This mechanism selects higher-reputation ICN consensus nodes as proposers for checkpoint blocks, mitigating the impact of network latency fluctuations on block propagation. Additionally, RepuICN improves block propagation efficiency through ICN multicast and caching techniques. Simulation results show that, under identical conditions with a network of 5000 nodes and 2% ICN nodes, RepuICN reduces broadcast latency by 17% for regular blocks and 61.4% for checkpoint blocks and achieves 3.4 times higher transaction throughput than Casper FFG.
This study proposes a decentralized framework that merges smart contract based Decentralized Finance (DeFi) protocols and traditional Enterprise Resource Planning (ERP) systems to provide secure, automatic, and verifiable transaction execution. It constructs an additional middleware interface to guarantee interoperability between ERP modules and blockchain networks that utilize smart contracts for procurement, finance, and asset management modules. The system was tested empirically within a hybrid testbed of chains with Ethereum Virtual Machine (EVM) compatibility simulation executing ERP transaction testing on a simulated environment with physical hardware. According to quantitative assessment results, performance increased, achieving a 38% increase in transaction throughput, a 27% decrease in execution costs, increased trust and traceability due to cryptographic audit trails, and improved auditability. The research highlights the potential of DeFi integrated ERP systems for decentralized enterprise finance systems as a scalable secure replacement to centralized enterprise finance systems.
In recent years, the proliferation of malware has reached unprecedented levels, leading to escalating cybercrime costs. Signatures extracted by static analysis of files have been widely adopted for malware detection: vendors maintain databases of known malware signatures that are shared with registered users. The recent literature has proposed the use of private and consortium (thus, permissioned) blockchains for spreading signatures among blockchain users. These approaches require controlled access to enhance trust and accountability but restrict the widespread sharing of up-to-date signatures because users must be registered. In this paper, we present a novel technique that leverages a public blockchain to enable the massive dissemination of malware signatures among any users since a public blockchain is permissionless. On the other hand, the use of a public blockchain introduces new challenges related to security and data privacy, which our solution solves. The main benefit and outcome of our solution is that any users can securely access and verify malware signatures facilitating real-time detection of malicious files. We implemented our solution in Ethereum and exploited a smart contract written in Solidity to demonstrate that our approach is highly cost-effective.