Khiem H. G, Huong H. L, Phuc N. T, Khoa T. D · 14 authors
The escalating demand for blood and its derivatives in the medical field underpins its indispensable nature for disease diagnosis and therapy. Such essential life-giving components are irreplaceable, necessitating a continuous reliance on voluntary blood donors. Existing methodologies primarily address the challenges of blood storage and its logistical distribution among healthcare centers. These conventional strategies lean towards centralized systems, often compromising data transparency and accessibility. Notably, there remains a significant gap in incentivizing and raising awareness among potential and existing donors regarding the life-saving act of blood donation. Recognizing these challenges, we introduce a robust and innovative framework that harnesses the potential of Blockchain technology, coupled with the power of smart contracts. Furthermore, to foster a sustainable blood donation ecosystem, we advocate the shift from traditional paper-based recognition to digitized donor acknowledgment using Non-Fungible Tokens (NFTs). Our novel approach encapsulates four key areas: (a) Introduction of a supply chain oversight mechanism for blood and its derivatives through Blockchain and smart contracts; (b) Development of a digital certification system for blood donors utilizing NFTs; (c) Execution of our suggested framework via smart contracts, offering a tangible proof-of-concept; and (d) Assessment and implementation of the proof-of-concept across four prominent platforms: ERC721 (ETH’s NFT), and the Ethereum Virtual Machine (EVM) employing the Solidity language – this encompasses the BNB Smart Chain, Fantom, Polygon, and Celo, aiming to discern the optimal platform compatible with our innovative framework.
During the last couple of years, non-fungible tokens became the most prominent implementation of blockchain technology apart from cryptocurrencies. This is mainly due to their recent association with digital art, but the application of non-fungible tokens has been in the focus of researchers since the appearance of Blockchain 2.0. It was usually tightly coupled with the research on possible applications of blockchain technology in some real-life applications, such as land administration, healthcare, or supply chain management. Since the initial release of the Ethereum blockchain in 2015, until 2022, more than 44 million smart contracts have been created, and out of those that are still active, more than 70% are based on some prominent templates. In the Ethereum blockchain, the creation of nonfungible tokens is usually based on Ethereum Request for Comments 721. In this paper, the authors are proposing the creation of a new standard that would support fractional ownership of non-fungible tokens. Fractional ownership is necessary so non-fungible tokens and blockchain technology could be applied to an even wider number of use cases. This paper also presents an example of a possible implementation of the newly proposed standard in the Solidity programming language.
Since the Merge update upon which Ethereum transitioned to Proof of Stake, it has been touted that it resulted in lower power consumption and increased security. However, even if that is the case, can this state be sustained? In this paper, we focus on the potential impact of competition with other smart contract platforms on the price of Ethereum's native currency, Ether (ETH), thereby raising questions about the safety and sustainability purportedly brought about by the design of Proof of Stake.
With the growing interest in cryptocurrency and blockchain by renowned companies environmental issues are also being highlighted due to the exhaustion of huge amounts of energy. This paper aims to give a primary overview of how cryptocurrencies are created by using the proof of work method and how blockchain works. Statistical references have been given to elaborate on how this process consumes high energy and increases carbon footprint causing harm to the environment. In comparison, the proof of stake method has established itself as an eco-friendly alternative. Ethereum has already manifested that using PoS can resolve the problem and is also safe for the environment.
The volume of information on the internet is currently rising dramatically. Social media platforms/e-commerce market place is producing a lot of data, including reviews, comments, and opinions, every day. As there are a number of fake reviews should incorporate Spam detection to produce a genuine opinion. Fake reviews are growing problem in online shopping, and they have a significant impact on consumer’s decision-making. Many people today base their decisions when choosing a product or service on social media opinions. Because so many false or phoney evaluations have been written by businesses or individuals for a variety of reasons, detecting opinion spam is a difficult and time-consuming task. They produce fictitious reviews to deceive users or automated detection systems by elevating or degrading the reputations of their target products in order to elevate or lower them. In this article, we’ll regulate it by leveraging blockchain technology to make the review system more authentic by allowing only legitimate product purchasers to submit evaluations using their account credentials. We use the Ethereum blockchain to authenticate user credentials, and we only permit verified users to purchase things. Also, we only permit customers to leave reviews or comments on products, ensuring that the reviews are accurate.
Smart contracts based on blockchain are widely used in finance, management, Internet of Things, healthcare, and other fields. However, with the rapid development of smart contracts, the corresponding security vulnerability attack cases occur frequently. Existing Ethereum smart contract vulnerability detection tools based on static analysis techniques rely too much on expert rules, for this reason, this paper proposes an Ethereum smart contract vulnerability detection method SCSVM based on support vector machine technology. A representation of smart contracts is constructed based on the word-to-vector technique, the features of Ethereum smart contracts are extracted based on the support vector machine technique, and these features are combined to identify vulnerabilities. Experiments on Smartbugs and Smartbugs-wild show that SCSVM is significantly effective. It achieves a detection accuracy of 87.51%, outperforming five typical static analysis vulnerability detection tools in terms of F1-score. To alleviate the problems of deep learning methods over-relying on large-scale data to train models and collecting a large number of smart contract attack samples in a short period, this paper proposes a basic learner-meta-learner framework, SCLMF. solc-based acquisition of the bytecode of Ethereum smart contract Solidity, on which smart contract representations are constructed via Python and the use of SCLMF for vulnerability detection. The experiments on WScrawlD show that SCLMF has a certain detection effect. Also, to further verify the effectiveness of SCLMF, experiments were conducted on Omniglot, and the detection accuracy was 96.7% and 98.5% under 5-way 1-shot and 5-way 5-shot conditions, respectively, which exceeded Memory-Augmented Neural Networks and CONVOLUTIONAL SIAMESE NETS. In summary, the experiments proved the effectiveness of SCSVM and SCLMF in Ethereum smart contract vulnerability detection.
Access control to patient information has become increasingly important in healthcare systems. It is vital to enhance the security of healthcare systems to avoid data loss despite the various security policies imposed by healthcare management. The issue needs to be resolved with a comprehensive secure framework, which allows users to access data according to their level of confidentiality. This article presents a solution by imposing multi-level security in e-health systems by integrating the Lattice-Based Access Control (LBAC) model and blockchain-based smart contract mechanisms. These mechanisms provide security levels in compliance with data access restrictions among users and resources while maintaining compliance security levels. By using LBAC, you can provide multilevel protection for access control restrictions, whereas smart contracts are used to ensure the transaction process in a decentralized system via an agreement between the parties. A smart contract validates every user and performs the authentication process in the envisioned model, which uses the Ethereum Virtual Machine (EVM). In the blockchain network, the patient’s e-health details are accessed and stored as immutable blocks. Comparing the proposed scheme with existing benchmarking methods reveals that the proposed scheme preserves privacy, maintains transparency, provides an authentication process, maintains data integrity, and provides multilevel access control security. The proposed model performs better than other existing models. As a result, lattice-based access control enhances the security of e-health records.
Grzegorz Dudek, Piotr Fiszeder, Paweł Kobus, Witold Orzeszko
Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of cryptocurrency price fluctuations is crucial for effective portfolio management and contributes to the stability of the financial system by identifying potential threats and developing risk management strategies. The objective of this paper is to provide a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Monero. Several models and forecasting methods are compared in terms of their forecasting accuracy, i.e., HAR (heterogeneous autoregressive), ARFIMA (autoregressive fractionally integrated moving average), GARCH (generalized autoregressive conditional heteroscedasticity), LASSO (least absolute shrinkage and selection operator), RR (ridge regression), SVR (support vector regression), MLP (multilayer perceptron), FNM (fuzzy neighbourhood model), RF (random forest), and LSTM (long short-term memory). The realized variance calculated from intraday returns is used as the input variable for the models. In order to assess the predictive power of the models considered, the model confidence set (MCS) procedure is applied. Our experimental results demonstrate that there is no single best method for forecasting volatility of each cryptocurrency, and different models may perform better depending on the specific cryptocurrency, choice of the error metric and forecast horizon. For daily forecasts, the method that is always found in a set of best models is linear SVR, while for weekly forecasts, there are two such methods, namely FNM and RR. Furthermore, we show that simple linear models such as HAR and ridge regression, perform not worse than more complex models like LSTM and RF. The research provides a useful reference point for the development of more sophisticated models.
As one of the most popular blockchain platforms supporting smart contracts, Ethereum has caught the interest of both investors and criminals. Differently from traditional financial scenarios, executing Know Your Customer verification on Ethereum is rather difficult due to the pseudonymous nature of the blockchain. Fortunately, as the transaction records stored in the Ethereum blockchain are publicly accessible, we can understand the behavior of accounts or detect illicit activities via transaction mining. Existing risk control techniques have primarily been developed from the perspectives of de-anonymizing address clustering and illicit account classification. However, these techniques cannot be used to ascertain the potential risks for all accounts and are limited by specific heuristic strategies or insufficient label information. These constraints motivate us to seek an effective rating method for quantifying the spread of risk in a transaction network. To the best of our knowledge, we are the first to address the problem of account risk rating on Ethereum by proposing a novel model called RiskProp, which includes a de-anonymous score to measure transaction anonymity and a network propagation mechanism to formulate the relationships between accounts and transactions. We demonstrate the effectiveness of RiskProp in overcoming the limitations of existing models by conducting experiments on real-world datasets from Ethereum. Through case studies on the detected high-risk accounts, we demonstrate that the risk assessment by RiskProp can be used to provide warnings for investors and protect them from possible financial losses, and the superior performance of risk score-based account classification experiments further verifies the effectiveness of our rating method.
Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based me... | Find, read and cite all the research you need on Tech Science Press
In recent years, with the great success of pre-trained language models, the pre-trained BERT model has been gradually applied to the field of source code understanding. However, the time cost of training a language model from... | Find, read and cite all the research you need on Tech Science Press
Sur Singh Rawat, Youseef Alotaibi, Nitima Malsa, Vimal Gupta
Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of industries, including monitoring, agriculture, surveillance, and supply chain. However, their widespread use also poses significant challenges, such as public safety, privacy, and cybersecurity. Cyberattacks, targeting UAVs have become more frequent, which highlights the need for robust security solutions. Blockchain technology, the foundation of cryptocurrencies has the potential to address these challenges. This study suggests a platform that utilizes blockchain technology to manage drone operations securely and confidentially. By incorporating blockchain technology, the proposed method aims to increase the security and privacy of drone data. The suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain transactions. The MetaMask wallet for Ethbalance is necessary for BCT transactions. The present research finding shows that the proposed approach’s efficiency and security features are superior to existing methods. This study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various industries. The proposed platform’s security measures could mitigate privacy concerns, minimize cyber security risk, and enhance public safety, ultimately promoting the widespread adoption of UAVs. The results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication, privacy preservation, confidentiality, integrity, and access control.
Ethereum is one of the first blockchains executing smart contracts (financial applications directly executed on the ledger using a virtual machine). The performance of the Ethereum virtual machine has become a bottleneck caused by high volumes of transactions due to blockchain applications, including decentralized finance and non-fungible tokens. Hence, there is a need to detail the execution characteristics of the Ethereum virtual machine before its performance can be improved. This work introduces an off-line Ethereum virtual machine tracer called EVMTracer that produces runtime data dependence graphs from executed transactions as an alternative program representation. From the runtime dependence graphs, we can deduce valuable metrics about program execution characteristics, including the degree of parallelization and computational redundancies in smart contracts. Our experiments encompass all blocks up to 12M on the Ethereum mainnet. We found a geometric mean of 1.90× theoretical maximum speedup when executing the smart contracts in parallel and identified 34.97% of SLOAD instructions as redundant.
Nowadays, traditional payment methods such as cash, cheques, credit/debit cards, and bank transfers are commonly used for financial transactions across a wide range of industries, including healthcare, real estate, supply chain, gaming, music, finance, retail, agriculture, tourism, pharmaceuticals, and the education sector too. However, these methods often involve transaction fees, potential delays, and security concerns. These methods lack transparency and traceability features, making tracking, authenticating, and verifying payments and their associated records difficult. To overcome these issues, we propose a Non-Fungible Token (NFT) based online payment system that leverages blockchain technology (BT) features and security to revolutionize financial expenses in the education sector. We also present wallet generation, fee tokenization and NFT generation processes. These processes utilize Ethereum and an interplanetary file system (IPFS) to store metadata information associated with fee payment details securely. We use Remix IDE, MetaMask wallet, and Sepolia test network to deploy and analyze the proposed system's results. We have also used the JMeter simulation toolkit to conduct comprehensive experiments to evaluate the performance of the proposed system in terms of throughput, latency, and response time. Our assessment identifies the increase in performance parameters with the number of users. This initiative will positively contribute towards addressing the issues of financial transactions in the education sector.
The advent of blockchain, smart contracts, and Web3 has empowered new concepts for equity partnerships with autonomous operating systems and democratic corporate governance. This paper explores 2,377 of such new partnerships and uses detailed transaction data (from 2017 through 2022) to examine the performance of so-called decentralized autonomous organizations (DAOs) on Ethereum. As a result, I find that DAOs with greater participation rates in voting are associated with superior performance. Small members are a prevalent and important class of investors, while the degree of decentralization in DAOs (ownership concentration) plays only a minor role in firm valuation. Overall, DAOs are an effective organizational structure, when members take an active interest in the venture.
In the Uniswap Protocol, a price oracle is a tool used to view price information about a given asset over time and enables developers to build highly decentralized protocols with quantifiable manipulation costs using price feeds. With the adoption of Proof of Stake (PoS), oracles are theoretically less secure because a malicious validator knows whether they control the next block created, allowing them to avoid value lost to back-running an attempted oracle manipulation. However, manipulation on most Uniswap v3 TWAP oracles is not currently possible, because bad actors both need to source vast amounts of capital and then earn enough to make up for value lost to fees. This paper studies the difficulty, potential cost, and likelihood of oracle manipulations on Uniswap v3 under Ethereum PoS. We also discuss potential future innovations to create the next generation of PoS manipulation resistant oracles.
This paper presents a dynamic equilibrium model of Ethereum's macroeconomy. The model captures agents' decisions regarding ETH holdings, staking, and the use of blockspace on both the Ethereum mainnet and Layer 2 networks. The ETH supply evolves according to protocol rules. The model's long-run behavior is characterized analytically, and key properties of the staking share and the price of ETH are derived. The model is calibrated using market data on ETH prices and transaction fees. Alternative issuance curves are evaluated for their effectiveness in managing staking levels.
Franck Cassez, Joanne Fuller, Milad K. Ghale, David J. Pearce · 5 authors
The Ethereum protocol implements a replicated state machine. The network participants keep track of the system state by: 1) agreeing on the sequence of transactions to be processed and 2) computing the state transitions that correspond to the sequence of transactions. Ethereum transactions are programs, called smart contracts, and computing a state transition requires executing some code. The Ethereum Virtual Machine (EVM) provides this capability and can execute programs written in EVM bytecode. We present a formal and executable semantics of the EVM written in the verification-friendly language Dafny: it provides (i) a readable, formal and verified specification of the semantics of the EVM; (ii) a framework to formally reason about bytecode.
The popularity of blockchain technology has bootstrapped many “Web3” applications, e.g., Ethereum and IPFS, that apply distributed ledger technology to store transactions. The amount of transactions generated and stored in such Web3 applications is significant and, in its raw form, usually not searchable by users. Existing Web3 transaction indexing and search engines are predominantly centralized and, therefore, can manipulate search results or censor particular queries. With the proliferation of Web3 transactions and applications, a decentralized and censorship-resistant search primitive is becoming essential. We present DeScan, a decentralized and censorship-resistant indexing and search engine for Web3. Users index their local Web3 transactions using custom rules that output triplets. Generated triplets are bundled in a distributed transaction graph that is searchable by other users. To coordinate search and distribute the storage of the transaction graph over peers in the network, we build upon a Skip Graph (SG) data structure. Since the Skip Graph does not provide any resilience against adversarial peers that censor searches, we propose four modifications to improve its robustness. We implement DeScan and conduct experiments with up to 12 800 peers and 10 million Ethereum transactions. Our experiments show that DeScan with our modifications enabled can tolerate 20% adversarial peers and 35% unresponsive peers without disruption. Moreover, we find that searches in DeScan are usually completed well within a second, even when the network grows. Finally, we show that storage and network costs are evenly distributed amongst peers as the network grows.
The explosive growth of decentralized finance (DeFi) has revolutionized the accessibility and functionality of financial services. To gain valuable insights into this evolving ecosystem, analyzing transaction data using network analysis methodology proves to be highly effective. Network analysis allows us to uncover intricate relationships, patterns, and communities within the DeFi market by examining participant transactions. We model nodes group representing actors or wallet addresses, and edges represent transactions between wallet addresses. This study focuses on three prominent DeFi token-based Ethereum protocols: DAI, UNI, and WBTC. We analyzed 5,802,742 transaction data spanning from January 2022 to January 2023 of those three tokens. Using network topology metrics, we uncover market size, average transaction of each wallet address, market density, and reachability (diameter). We also detect the presence of transaction clusters and grouping quality using the modularity metric. At last, we employ centrality calculation to identify the most important wallet address and their role in the market. We gain insight by comparing those three tokens and reveal valuable insight into patterns and relationships, network dynamics, decentralized nature, and the presence of intermediaries in the token economy market. Our objective is to uncover hidden dynamics and trace asset flows, which can provide valuable information to market participants, regulators, and innovators seeking to optimize the DeFi infrastructure, ensure stability, and mitigate risks. Leveraging the power of network analysis offers the potential to shape the future of DeFi by enhancing efficiency, security, and accessibility, thereby fostering financial inclusion and empowering individuals and businesses globally.
Decentralized Finance (‘DeFi’) has gained tremendous momentum over the past three years by using novel approaches to disintermediating financial institutions in the provision of financial services. However, empirical research in this field is still rare, and a more comprehensive understanding of the domain is a missing component in academic research. This paper develops a taxonomy based on a comprehensive literature analysis to structure this emerging field systematically. The taxonomy includes three perspectives (strategy, organization, technology) and seven dimensions (blockchain, value proposition, token type, business process, price mechanism, protocol type, integration type) as well as thirty-six characteristics. The application of the taxonomy to 278 DeFi start-ups reveals that most of the DeFi start-ups focus on Ethereum (36.3%) and have a focus on analytics and automation (52%), while, surprisingly only a few incorporate decentralized governance approaches (3.3%), provide decentralized exchanges (14%) or integrate off-chain data.
Ethereum smart contracts are automated decentralized applications on the blockchain that describe the terms of the agreement between buyers and sellers, reducing the need for trusted intermediaries and arbitration. However, the deployment of smart contracts introduces new attack vectors into the cryptocurrency systems. In particular, programming flaws in smart contracts have been already exploited to lead to enormous financial loss. Hence, it is crucial to detect various vulnerability types in contracts effectively and efficiently. Existing vulnerability detection methods are limited in scope as they typically focus on one or a very limited set of vulnerabilities. Also, extending them to new vulnerability types requires costly re-design. \n \nIn this work, we develop ESCORT, a deep learning-based vulnerability detection method that uses a common feature extractor to learn generic bytecode semantics of smart contracts and separate branches to learn the features of each vulnerability type. As a multi-label classifier, ESCORT can detect multiple vulnerabilities of the contract at once. Compared to prior detection methods, ESCORT can be easily extended to new vulnerability types with limited data via transfer learning. When a new vulnerability type emerges, ESCORT adds a new branch to the trained feature extractor and trains it with limited data. We evaluated ESCORT on a dataset of 3.61 million smart contracts and demonstrate that it achieves an average F1 score of 98% on six vulnerability types in initial training and yields an average F1 score of 96% in transfer learning phase on five additional vulnerability types. To the best of our knowledge, ESCORT is the first deep learning-based framework that utilizes transfer learning on new vulnerability types with minimal model modification and re-training overhead. Compared with existing non-ML tools, ESCORT can be applied to contracts of arbitrary complexity and ensures 100% contract coverage. In addition, we enable concurrent detection of multiple vulnerability types using a single unified framework, thus avoiding the efforts of setting up multiple tools and greatly reducing the detection time. We will open source our dataset and the data labeling toolchain to facilitate future research.
Ethereum is a digital asset whose transactions are kept on a decentralized, globally accessible ledger. An Ethereum Blockchain owner's real identity is concealed behind a pseudonym termed an address. Because of this, Ethereum is frequently used in illegal activities like gambling and ransomware attacks because it is popularly believed to offer the highest level of anonymity. As a result, it is necessary to categorize the various malicious cybercriminal users' activities and addresses in the Ethereum Blockchain. The Blockchain's public data enables an in-depth analysis. Using supervised machine learning models including linear, non-linear, and ensemble learning models based on malicious and non-malicious activities, the classification of Ethereum Blockchain addresses is carried out in this paper. In this research work, cross-validation accuracy, recall, precision, and f1-score have been employed for the assessment. Findings indicate that linear and non-linear machine learning approaches are superior to ensemble learning for classifying Ethereum Blockchain addresses. The results also show that it is possible to discover the Ethereum Blockchain addresses of malicious users.