The Log-Structure Merged tree (LSM-tree) based key-value (KV) store has been widely adopted as the storage engine for blockchain systems, such as Ethereum, in which blockchain data are uniformly transformed into randomly distributed KV items for persistence. However, blockchain semantics are ignored during this process, making the blockchain storage suffer from heavy read/write amplification problems. Moreover, as the Ethereum network scales up, tremendous data further exacerbates its storage burden. Until now, most studies have focused on sharding, data archiving, decentralized distributed storage, etc., to mitigate the burden of the storage layer. However, the incompatibility between Ethereum semantics and the characteristics of the storage engine is ignored. In this paper, we present ChainKV, a new semantics-aware storage paradigm to improve the storage management performance for the Ethereum system. Firstly, based on Ethereum blockchain semantics, ChainKV separately stores different types of data in multiple storage zones in the KV store to mitigate the read/write amplification problem. Secondly, following the mechanism of the verification process in the authenticated data structure (ADS), a new ADS data transformer is proposed to exploit the data locality when persisting ADS. Moreover, a new space gaming caching policy is adopted to coordinate the cache space management for two independent storage zones. Finally, we propose an optional lightweight node crash recovery mechanism to eliminate functional redundancy between the Ethereum protocol and the storage engine. The experimental results indicate that ChainKV outperforms the prior Ethereum systems by up to 1.99× and 4.20× for synchronization and query operations, respectively
Ernesto Gómez-Marín, Luis Parrilla, Jose L. Tejero López, Diego P. Morales · 5 authors
In this work, a secure architecture to send data from an Internet of Things (IoT) device to a blockchain-based supply chain is presented. As is well known, blockchains can process critical information with high security, but the authenticity and accuracy of the stored and processed information depend primarily on the reliability of the information sources. When this information requires acquisition from uncontrolled environments, as is the normal situation in the real world, it may be, intentionally or unintentionally, erroneous. The entities that provide this external information, called Oracles, are critical to guarantee the quality and veracity of the information generated by them, thus affecting the subsequent blockchain-based applications. In the case of IoT devices, there are no effective single solutions in the literature for achieving a secure implementation of an Oracle that is capable of sending data generated by a sensor to a blockchain. In order to fill this gap, in this paper, we present a holistic solution that enables blockchains to verify a set of security requirements in order to accept information from an IoT Oracle. The proposed solution uses Hardware Security Modules (HSMs) to address the security requirements of integrity and device trustworthiness, as well as a novel Public Key Infrastructure (PKI) based on a blockchain for authenticity, traceability, and data freshness. The solution is then implemented on Ethereum and evaluated regarding the fulfillment of the security requirements and time response. The final design has some flexibility limitations that will be approached in future work.
Digital currency, as a representative product of blockchain technology, such as Bitcoin and Ethereum, has begun to have a far-reaching impact on traditional banking business, but also brought new challenges to financial stability. This article studies the financial risk assessment and pre-alarm model based on support vector machine (SVM) algorithm, aiming at accurately assessing the risks in digital currency market and providing timely pre-alarm. Through comparative experiments, it is found that the algorithm has obvious advantages in dealing with high-dimensional and nonlinear problems, and can accurately classify and warn financial risks. The accuracy of the model is as high as 96.88%, and the average absolute error is reduced by 40.22%, which proves the effectiveness and superiority of the model. Generally speaking, the financial risk assessment and pre-alarm model studied in this article provides a scientific and effective method for digital currency market risk management, which has important application value. Regulators need to invest more resources and energy to monitor and analyze the dynamic changes in the digital currency market and reduce financial risks.
Bu çalışmada altın ile kripto paralar arasındaki ilişkiler doğrusal olmayan modeller ile kapsamlı olarak araştırılmaktadır. Kripto paraları temsilen dijital altın olarak da adlandırılan en büyük kripto para Bitcoin ve en büyük akıllı kontrat platformu Ethereum çalışmada birlikte ele alınmaktadır. Hepsağ (2021) doğrusal olmayan eşbütünleşme testi bulgularına göre, ilgili değişkenler arasında çok zayıf düzeyde uzun dönemli ilişki, doğrusal olmayan Granger nedensellik testi sonuçlarına göre ise iki yönlü nedensellik ilişkisi tespit edilmiştir. Son olarak düzeltilmiş dinamik koşullu korelasyon (cDCC-GARCH) sonuçlarına göre altın ve kripto paralar arasında genellikle pozitif ve sıfıra yakın korelasyon bulunduğu, ancak COVID-19 salgınının görüldüğü 2020 yılı boyunca değişkenler arasındaki korelasyon ilişkisinin daha da arttığı belirlenmiştir. Elde edilen bulgular yatırımcılar için portföy çeşitlendirmesi, risk yönetimi ve piyasa öngörüsü açısından önemli bilgiler sunmaktadır.
Guocheng Zhu, Debiao He, Haoyang An, Min Luo · 5 authors
Abstract After the Ethereum DAO attack in 2016, which resulted in significant economic losses, blockchain governance has become a prominent research area. However, there is a lack of comprehensive and systematic literature review on blockchain governance. To deeply understand the process of blockchain governance and provide guidance for the future design of the blockchain governance model, we provide an in-depth review of blockchain governance. In this paper, first we introduce the consensus algorithms currently used in blockchain and relate them to governance theory. Second, we present the main content of off-chain governance and investigate two well-known off-chain governance projects. Third, we investigate four common on-chain governance voting techniques, then summarize the seven attributes that the on-chain governance voting process should meet, and finally analyze four well-known on-chain governance blockchain projects based on the previous research. We hope this survey will provide an in-depth insight into the potential development direction of blockchain governance and device future research agenda.
Airdrops are a popular mechanism used by blockchain protocols to bootstrap communities, reward early adopters, and decentralize token distribution. Despite their widespread adoption, the effectiveness of airdrops in achieving long-term user engagement and ecosystem growth remains poorly understood. In this paper, we present the first comprehensive empirical study of nine major airdrops across Ethereum and Layer-2 ecosystems. Our analysis reveals that a substantial share of tokens--up to 66% in some cases--are rapidly sold, often in recipients' first post-claim transaction. We show that this behavior is largely driven by "airdrop farmers," who strategically optimize eligibility criteria to extract value without contributing meaningfully to the ecosystem. We complement our quantitative findings with a case study of the Arbitrum airdrop, illustrating how short-term activity spikes fail to translate into sustained user involvement. Based on these results, we discuss common design pitfalls--such as Sybil vulnerability, poor incentive alignment, and governance token misuse--and propose actionable guidelines for designing more effective airdrop strategies.
Chuangming Zhou, Yang Zhou, Shaohua Yue, Bona Xuan · 5 authors
In the era of big data, facilitating efficient data flow is of paramount importance. Governments and enterprises worldwide have been investing in the big data industry, promoting data sharing and trading. However, existing data trading platforms often suffer from issues like privacy breaches, single points of failure, data tampering, and non-transparent transactions due to their reliance on centralized servers. To address these challenges, blockchain-based big data transaction models have been proposed. However, these models often lack system integrity and fail to fully meet user requirements while ensuring adequate security. To overcome these limitations, this paper presents an Ethereum-based big data trading model that establishes a comprehensive and secure trading system. The model aims to provide users with more convenient, secure, and professional services. Through the utilization of smart contracts, users can efficiently match data and negotiate prices online while ensuring secure data delivery through encryption technologies. Additionally, the model introduces a trusted third-party entity that offers professional data evaluation services and actively safeguards user data ownership in the event of disputes. The implementation of the model includes the development of smart contracts and the necessary machine learning code, followed by rigorous testing and validation. The experimental results validate the effectiveness and reliability of our proposed model, demonstrating its potential to ensure effective and secure big data trading.
<abstract><p>An empirical investigation was held regarding whether topological properties associated with point clouds formed by cryptocurrencies' prices could contain information on (locally) explosive dynamics of the processes involved. Those dynamics are associated with financial bubbles. The Phillips, Shi and Yu <sup>[<xref ref-type="bibr" rid="b33">33</xref>,<xref ref-type="bibr" rid="b34">34</xref>]</sup> (PSY) timestamping method as well as notions associated with the Topological Data Analysis (TDA) like persistent simplicial homology and landscapes were employed on a dataset consisting of the time series of daily closing prices of the Bitcoin, Ethereum, Ripple and Litecoin. The note provides some empirical evidence that TDA could be useful in detecting and timestamping financial bubbles. If robust, such an empirical conclusion opens some interesting paths of further research.</p></abstract>
In blockchains, mempool controls transaction flow before consensus, denial of whose service hurts the health and security of blockchain networks. This paper presents MPFUZZ, the first mempool fuzzer to find asymmetric DoS bugs by exploring the space of symbolized mempool states and optimistically estimating the promisingness of an intermediate state in reaching bug oracles. Compared to the baseline blockchain fuzzers, MPFUZZ achieves a > 100x speedup in finding known DETER exploits. Running MPFUZZ on major Ethereum clients leads to discovering new mempool vulnerabilities, which exhibit a wide variety of sophisticated patterns, including stealthy mempool eviction and mempool locking. Rule-based mitigation schemes are proposed against all newly discovered vulnerabilities.
The burgeoning domain of the Internet of Vehicles (IoV), a subset of the Internet of Things (IoT), promises to revolutionize transportation through enhanced safety, efficiency, and environmental sustainability. By amalgamating technologies like sensors and cloud computing, the IoV paves the way for optimized traffic management, heightened vehicle safety, and the birth of novel business paradigms. However, this growth is shadowed by significant security concerns, especially in the communication and payment sectors. Addressing the pressing need for secure Vehicle to Everything (V2X) communications and payments amidst rising cyber threats, this research introduces the Ethereum based Secure Payment and Communication Solution (ESP2CS). Utilizing Ethereum as a middleware, ESP2CS ensures robust and secure V2X interactions. The solution is complemented by an Android Auto application for vehicles, streamlining inter vehicle communication, parking space detection, and transaction management. Furthermore, dedicated Android applications are developed for parking space renters and the parking IoT system. Preliminary evaluations underscore ESP2CS's superior cost effectiveness, integrity and consistency over contemporary solutions, with Ethereum bolstering both security and efficiency.
Nir Chemaya, Lin William Cong, Emma Joergensen, Dingyue Liu · 5 authors
Decentralized Finance (DeFi) is revolutionizing traditional financial services by enabling direct, intermediary-free transactions, thereby generating a substantial volume of open-source transaction data. This evolving DeFi landscape is particularly influenced by the emergence of Layer 2 (L2) solutions, which are poised to enhance network efficiency and scalability significantly, surpassing the existing capabilities of Layer 1 (L1) infrastructures. However, the detailed impact of these L2 solutions has been somewhat obscured due to a dearth of transaction data indices that can provide in-depth economic insights for empirical research. This study seeks to address this critical gap by conducting a comprehensive analysis of raw transactions sourced from Uniswap, a central decentralized exchange (DEX) within the DeFi ecosystem. The dataset encompasses an extensive collection of over 50 million transactions from both L1 and L2 networks. Additionally, we have curated a wide-ranging repository of daily indices derived from transaction trading data across prominent blockchain networks, including Ethereum, Optimism, Arbitrum, and Polygon. These indices shed light on crucial network dynamics, such as adoption trends, evaluations of scalability, decentralization metrics, wealth distribution patterns, and other key aspects of the DeFi landscape. This rich dataset serves as an invaluable tool, enabling researchers to dissect the complex interplay between DeFi and Layer 2 solutions, thus enhancing our collective understanding of this rapidly evolving ecosystem. Its notable contribution to the data science pipeline includes the implementation of a flexible, open-source Python framework, enabling the dynamic calculation of decentralization indices, customizable to specific research requirements. This adaptability makes the dataset particularly suitable for advanced machine learning applications, including deep learning, thereby solidifying its role as a critical asset in shaping Blockchain as the foundational infrastructure for the intelligent Web3 ecosystem.
Nir Chemaya, Lin William Cong, Emma Jorgensen, Dingyue Liu · 5 authors
Decentralized Finance (DeFi) is reshaping traditional finance by enabling direct transactions without intermediaries, creating a rich source of open financial data. Layer 2 (L2) solutions are emerging to enhance the scalability and efficiency of the DeFi ecosystem, surpassing Layer 1 (L1) systems. However, the impact of L2 solutions is still underexplored, mainly due to the lack of comprehensive transaction data indices for economic analysis. This study bridges that gap by analyzing over 50 million transactions from Uniswap, a major decentralized exchange, across both L1 and L2 networks. We created a set of daily indices from blockchain data on Ethereum, Optimism, Arbitrum, and Polygon, offering insights into DeFi adoption, scalability, decentralization, and wealth distribution. Additionally, we developed an open-source Python framework for calculating decentralization indices, making this dataset highly useful for advanced machine learning research. Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.
Yue Xue, Dunqiu Fan, Shen Su, Jialu Fu · 7 authors
Decentralized finance (DeFi) is a general term for a series of financial products and services. It is based on blockchain technology and has attracted people’s attention because of its open, transparent, and intermediary free. Among them, the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention. However, the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years. Herein, we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues. To that end, we investigate the Ethereum-based DeFi security issues: 1) inherited from the real-world financial system, which can be solved by macro-control; 2) induced by the problems of blockchain architecture, which require a better blockchain platform; 3) caused by DeFi invented applications, which should be focused on during the project development. Based on that, we further discuss the current solutions and potential directions of DeFi security. According to our research, we could provide a comprehensive vision to the research community for the improvement of Ethereum-based DeFi ecosystem security.
Saidu Sokoto, Michał Król, Vladimir Stanković, Étienne Rivière
Distributed Hash Tables (DHTs) serve as the backbone of numerous modern decentralized systems like the InterPlanetary File System (IPFS) and Ethereum. As these systems evolve and expand, there is a growing need to enhance and optimize their underlying network support. In response to these challenges, we embark on the development of a new class of DHTs, marked by efficiency, security, and suitability for real-world deployments. We achieve this by making changes in the routing procedures, incorporating latency-aware routing, and harnessing recent hardware advancements.
It is observed that there are several artists who are developing marvelous products but their product needs international exposure. These artists have limited market if they are going to promote and sell product. Moreover the there is lack of premium customer in rural areas. Thus artist who lives in rural area does not get proper incentive and significance. NFT is the only way to provide such artist global market where such artists may promote their art work. With the advent of blockchain technology NFT creation and marketing has been increased. Thus there are several international; market place such as Opensea and Young parrot that allows creation of NFT over Matic, Satoshi core, Ethereum and BNB smart chain. 9NFTMANIA proposed a lifestyle that is named as NFT culture. In these culture greetings, invitations, certificates, membership card would be made in form of NFT. In this way secure transfer of digital asset could be made and this NFT would have particular value. Even if a person wants to thank someone or want to say good morning then he should transfer NFT to other person's wallet. Moreover the supply of such greeting NFT would be limited thus there remains expectation of raise in price. On other hand holder of NFT would be able to access premium web services when web 3.0 mechanisms are applied to verify NFT holder in Metaverse.
Jéfferson Augusto Colombo, Tanzina Akhter, Peter Wänke, Md. Abul Kalam Azad · 7 authors
In the rapidly evolving domain of digital finance, the interplay between cryptocurrencies and external variables such as financial and social media indicators warrants thorough examination. This investigation employs a novel, entropy-weighted Multiple Attribute Decision Making (MADM) model to decipher these intricate relationships. The study's foundation is an expansive dataset, meticulously compiled to encompass a broad spectrum of financial data alongside diverse social media indicators. Central to this analysis is the employment of the Stepwise Weight Assessment Ratio Analysis (SWARA) method, meticulously applied to ascertain the relative importance of various social media indicators. Complementing this, the Complex Proportional Assessment (COPRAS) methodology is adeptly utilized to derive utility functions for each cryptocurrency under scrutiny. The analytical prowess of neural network regressions is harnessed to delineate the influence exerted by a multitude of financial indicators on these utility functions. The findings of this research are pivotal in understanding the dynamics within the cryptocurrency market. Bitcoin and Ripple emerge as pivotal entities, primarily functioning as primary conduits for market shocks. In contrast, Ethereum is identified as a stabilizing force, predominantly absorbing such fluctuations. A nuanced aspect of this study is the differential impact of social media indicators on various cryptocurrencies. Bitcoin and Ethereum display a negative correlation with these indicators, suggesting a complex, possibly inverse relationship with social media dynamics. Conversely, Litecoin, Dogecoin, and Ripple exhibit a positive responsiveness, indicating a heightened susceptibility to social media attention, sentiment, and prevailing uncertainty.
Anak Agung Gde Agung, Irna Yuniar, Robbi Hendriyanto
The halal industry today has achieved rapid development. Halal product is mandatory for Muslims and a big business for Indonesia. For others, it affirms the product's quality assurance and becomes a trending lifestyle. The product owner must submit an application and undergo several processes to obtain a halal certificate. However, there are challenges in the certification process and documentation. The proposed system automates the flow between certification processes through digital signing and stores the certificate and fatwa file. The study investigates the utilization of blockchain to manage the process and the integration of decentralized storage (IPFS) to store the digital version of the fatwa and certificate. A smart contract is designed and deployed on the Ethereum blockchain, and the transaction time and cost are analyzed. A smart contract enforces that certain actions are executed once the required conditions are fulfilled. The proposed system would cost 24.6 USD and require 227 seconds on average for the system setup. Each submission requires 9.86 USD and takes 92 seconds on average. Verification is free, and the average result can be obtained in one second. The appointed officer sets each entity to interact with the contract, and the digital documents (fatwa and certificate) are available online using IPFS. Progress of the certification is transparent to the public, increasing the public's trust. The study demonstrates a smart contract's capability to manage a product's certification process.
Task features. Smart-contracts are programs that are stored in a distributed registry and execute code written in them in response to transactions addressed to them. Such smart- contracts are written in the Solidity programming language, which has a specific structure and syntax. The language was developed for the Ethereum platform. Having a specific structure, such languages are prone to certain vulnerabilities, the use of which can lead to large financial losses. Task statement. In this paper, a Deep Learning (DL) model is used to detect the vulnerabilities. Using the chosen approach and a properly specified input data structure, it is possible to detect complex dependencies between various program variables that contain vulnerabilities and bugs. Research results. Using well-defined experiments, this approach was investigated to better understand the model and improve its performance. The developed model classified vulnerabilities at the string level, using the Solidity corpus of smart-contracts as input data. The application of the DL model allows vulnerabilities of varying complexity to be identified in smart-contracts. Conclusions. Thus, the pipeline developed by us can capture more internal code information than other models. Information from software tokens, although semantically incapable of capturing vulnerabilities, increases the accuracy of models. The interpretability of the model has been added through the use of the attention mechanism. Operator accounting has shown significant performance improvements.
We obtain daily data of Bitcoin, Ethereum, Travala token, Kemacoin and Guider to investigate the implications of history's most famous five heists on travel and tourism. We find a statistically significant spillover effect in the cryptocurrency and tourism token markets with a limited impact on travel and tourism companies' stock prices. We also find evidence of herding behaviour and observe that overall market quality deteriorated because of the heists. To deal with these negative implications, we propose implementing tools based on artificial intelligence algorithms, emphasising the two leading cryptocurrencies – Bitcoin and Ethereum. Tracking major crypto wallets and ‘whales’ can help regulators identify potential hacks and mitigate systemic risk caused by spillovers in cryptocurrency markets.
Phishing scams have become the most serious type of crime involved in Ethereum. However, existing methods ignore the natural camouflage and sparse distribution of phishing scams in Ethereum leading to unsatisfactory performance, and they are also limited by the data scale which cannot be applied to real-world dynamic scenarios. In this paper, we propose a Transaction Graph Contrast network (TGC) to enhance phishing scam detection performance on Ethereum. TGC inputs subgraphs instead of the entire graph for training, which eases the model’s requirements for machine configuration and data connectivity. Motivated by phishing nodes are surrounded by normal nodes, we design the comparison between node-level to help phishing nodes learn the unique properties of themselves different from their neighbors. Observing the small number and sparse distribution of phishing nodes, we narrow the distance between phishing nodes by comparing node context-level structures, so as to learn universal transaction patterns. We further combine the obtained features with common statistics to identify phishing addresses. Evaluated on real-world Ethereum phishing scams datasets, our TGC outperforms the state-of-the-art methods in detecting phishing addresses and has obvious advantages in large-scale and dynamic scenarios.
Data science and artificial intelligence have become an indispensable part of scientific research. While such methods rely on high-quality and large quantities of machine-readable scientific data, the current scientific data infrastructure faces significant challenges that limit effective data curation and sharing. These challenges include insufficient return on investment for researchers to share quality data, logistical difficulties in maintaining long-term data repositories, and the absence of standardized methods for evaluating the relative importance of various datasets. To address these issues, this paper presents the Lennard Jones Token, a blockchain-based proof-of-concept solution implemented on the Ethereum network. The token system incentivizes users to submit optimized structures of Lennard Jones particles by offering token rewards, while also charging for access to these valuable structures. Utilizing smart contracts, the system automates the evaluation of submitted data, ensuring that only structures with energies lower than those in the existing database for a given cluster size are rewarded. The paper explores the details of the Lennard Jones Token as a proof of concept and proposes future blockchain-based tokens aimed at enhancing the curation and sharing of scientific data.
In this chapter, with a focus on the Ethereum blockchain, different development tools and environments are presented. The component functions of a sample Ethereum token standard are also discussed. This chapter closes with a discussion of different decentralized application architectures.
In international trade and financial exchanges, global cryptocurrencies have a prominent and effective role.In this paper, using the KOF index, the structure of financial globalization is extracted and the effect of the expansion of global cryptocurrencies is examined in the two components: Foreign Direct Investment and Portfolio Investment.The Ordinary Least Squares (OLS) model has been used separately for 2020, 2021, and 2022 to analyze the results of the expansion of global cryptocurrencies in the foreign direct investment component.Also, the price changes of Bitcoin and Ethereum from March 10, 2016, to the end of December 2022 have been used to investigate the effect of global cryptocurrencies in portfolio investment by applying Modern Portfolio Theory (MPT).Also, according to the availability of data in research sources, the data of 111 countries have been used.OLS estimated results suggest that the adoption and expansion of global cryptocurrencies has no significant relationship with Foreign Direct Investment.Also, using MPT, the results of portfolio optimization suggest that global cryptocurrencies improve the effectiveness of the selected portfolios, and with the same corresponding returns, the risk of the portfolios including global cryptocurrencies decreases as well.Therefore, the results emphasize the role of global cryptocurrencies in financial globalization only as crypto-assets.
Smart supply chain services rely on the utilization of massive amount of data collected by sensor networks deployed in different enterprises. Sensing as a Service (S2aaS) is a promising Internet of Things (IoT) business model pattern for data exchange. The current centralized IoT S2aaS models are not suitable for IoT big data exchange due to the issues on privacy disclosure, single point of failure, data security, performance, etc. In this paper, we propose a blockchain-based decentralized framework for IoT S2aaS for smart supply chain, which can ensure the IoT solution owners have full control of their data and securely exchange data with data consumers without intermediaries. We introduce the system model and the layered architecture of our proposed framework, based on which we give a concrete scheme, smart contract is used to perform the whole process of IoT S2aaS. We implement a software prototype on Ethereum. Experiment results show the validity and effectiveness of our proposed solution.