The Ethereum blockchain is a decentralized computing platform providing peer-to-peer financial services. Decentralized exchanges, which run on the blockchain, enable matching of buyers and sellers without any central third party, and are distinct from the centralized âoff-chainâ cryptocurrency markets often studied in the literature. The decentralized markets facilitate trade in cryptocurrencies and other digital assets and have daily turnovers of several billion dollars. In this paper, I study how arbitrageurs on the blockchain contribute to price discovery and price efficiency in decentralized âon-chainâ markets. I collect a transaction-level dataset of primary data from the Ethereum blockchain and cleanly identify a set of completed cross-exchange and triangular arbitrages. To investigate the speed at which arbitrage opportunities are eliminated, I study how sensitive arbitrage profits are to when the trades execute. I show that most arbitrage profits are made immediately after the occurrence of price anomalies, indicating that decentralized markets adjust fast after a shock to the no-arbitrage price.
Blockchain technology supports the generation and record of transactions, and maintains the fairness and openness of the cryptocurrency system. However, many fraudsters utilize smart contracts to create fraudulent Ponzi schemes for profiting on Ethereum, which seriously affects financial security. Most existing Ponzi scheme detection techniques suffer from two major restricted problems: the lack of motivation for temporal early warning and failure to fuse multi-source information finally cause the lagging and unsatisfactory performance of Ethereum Ponzi scheme detection. In this paper, we propose a dual-channel early warning framework for Ethereum Ponzi schemes, named Ponzi-Warning, which performs feature extraction and fusion on both code and transaction levels. Moreover, we represent a temporal evolution augmentation strategy for generating transaction graph sequences, which can effectively increase the data scale and introduce temporal information. Comprehensive experiments on our Ponzi scheme datasets demonstrate the effectiveness and timeliness of our framework for detecting the Ponzi contract accounts.
Abstract Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artistâs work in the first place. Additionally, while blockchains like Ethereum are ideal for smaller works of art, additional support is needed when the artwork is larger than is feasible to store on the blockchain. In this paper, we propose a fundraising mechanism that will help artists to gain financial support for their initiatives, and where the backers can receive a share of the profits in exchange for their support. We discuss our prototype implementation using the SpartanGold framework. We then discuss how this system could be expanded to support large NFTs with the 0Chain blockchain, and describe how we could provide support for ongoing storage of these NFTs.
Jan 1, 2022¡Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Tom Barbereau, Reilly Smethurst, Orestis Papageorgiou, Alexander Rieger ¡ 5 authors
Bitcoin and Ethereum are frequently promoted as decentralized, but developers and academics question their actual decentralization. This motivates further experiments with public permissionless blockchains to achieve decentralization along technical, economic, and political lines. The distribution of tokenized voting rights aims for political decentralization. Tokenized voting rights achieved notoriety within the nascent field of decentralized finance (DeFi) in 2020. As an alternative to centralized crypto-asset exchanges and lending platforms (owned by companies like Coinbase and Celsius), DeFi developers typically create non-custodial projects that are not majority-owned or managed by legal entities. Holders of tokenized voting rights can instead govern DeFi projects. To scrutinize DeFiâs distributed governance strategies, we conducted a multiple-case study of non-custodial, Ethereum-based DeFi projects: Uniswap, Maker, SushiSwap, Yearn Finance, and UMA. Our findings are novel and surprising: quantitative evaluations of DeFiâs distributed governance strategies reveal a failure to achieve political decentralization.
A blockchain is a list of data blocks as a publicly distributed ledger, which are linked together using cryptography. By allowing Turing-complete programming languages to implement smart contracts, recent blockchains such as Ethereum can reduce needs in trusted intermediators, arbitrations and enforcement costs. However, subtle errors in smart contracts have induced an enormous financial lossâfor examples, the DAO attack, Parity multisignature wallet attacks, and integer underflow/overflow attacks. To identify such errors in smart contracts, various researches are performed, which are based on static analysis and theorem proving. However, they only support inspection for pre-defined error patterns, or they cannot explore the whole searching space exhaustively or be fully automatic. Hence, in this paper, we propose a novel formal verification technique to analyze blockchain smart contracts by using ATL model checking. In our methodology, we represent the interaction between users and smart contracts into a two-player game and verify properties we want to check using MCMAS that is an efficient ATL model checker for multi-agent systems. Moreover, we present three case studies to show that our proposal can successfully identify subtle flaws in real world smart contracts.
G. Ravikumar, K. Venkatachalam, Mehedi Masud, Mohamed Abouhawwash
Recently internet of medical things (IoMT) act as a smart doctor using sensor wearableâs device in human body. This smart doctor device senses necessary medical data from human and transfer via network immediately to physician. It is important to transfer sensitive data very securely. Blockchain becomes trending technology to provide high security to both end users in the network. Traditionally security structure is relying on cryptographic techniques which is very expensive and takes more time in securely transmitting data. To overcome this issue, this paper builds a cost effective, blockchain with IoMT using fog-cloud computing. The aim of research is to provide cost effective healthcare services in the present system. This study develops an IoMT systems using fusion of scheduling techniques in blockchain. We propose a Smart Contract with Function Based Cost Efficient Task Scheduling (FTS-SCON) algorithm in blockchain framework. The proposed algorithm uses functions to schedule the task in the blockchain which is cost effective. With the help of cryptography based Blockchain schemes with smart contracts enables consistency and validation of data with symmetric cryptography. Simulation results shows the proposed outperform all existing elements regarding data security, validation by 10%, and cost of application execution by 30% in IoMT.
Standardized Ethereum tokens, e.g., ERC-20 tokens, have become the norm in fundraising (through ICOs) and kicking off blockchain-based DeFi applications. However, they require the user's wallet to hold both tokens and ether to pay the gas fee for making a transaction. This makes for a cumbersome user experience, and complicates, from the user perspective, the process of transitioning to a different smart-contract enabled blockchain, or to a newly launched blockchain. We formalize, instantiate, and analyze in a composable manner a system that we call Etherless Ethereum Tokens (in short, EETs), which allows the token users to transact in a closed-economy manner, i.e., having only tokens on their wallet and paying any transaction fees in tokens rather than Ether/Gas. In the process, we devise a methodology for capturing Ethereum token-contracts in the Universal Composability (UC) framework, which can be of independent interest.
Pedro Antonino, Juliandson Ferreira, Augusto Sampaio, A. W. Roscoe
Smart contract evolution is crucial for the success of decentralized applications, and current methods and processes are not well suited to handle these drivers of change, as the knowledge about the software is predominantly stored in informal documents. In addition, they are the building blocks of the âcode is lawâ paradigm: the smart contractâs code indisputably describes how its assets are to be managed - once it is created, its code is typically immutable. Faulty smart contracts present the most significant evidence against the practicality of this paradigm; they are well-documented and resulted in assets worth vast sums of money being compromised. To address this issue, the Ethereum community proposed (i) tools and processes to audit/analyse smart contracts, and (ii) design patterns implementing a mechanism to make contract code mutable. Individually, (i) and (ii) only partially address the challenges raised by the âcode is lawâ paradigm. In this work, we combine elements from (i) and (ii) to create a systematic framework that moves away from âcode is lawâ and gives rise to a new âspecifica- tion is lawâ paradigm. It allows contracts to be created and upgraded but only if they meet a corresponding formal specification. We explain how formal verification techniques can be used to ensure safety properties of smart contracts during their evolution. Although formal verification methods have the potential of being used in several application fields, we focus on ensuring compliance with its specifications. The process consists of three phases: Formal requirements specification, verification, and deployment. All steps are planned and executed in an integrated way and together they form a framework capable of fostering safe evolution and make it more reliable and secure. The framework is centered around a trusted deployer: an off-chain service that formally verifies and enforces specification conformance. We have proto- typed this framework, and investigated its applicability to contracts implementing three widely used Ethereum standards: the ERC20 Token Standard, ERC3156 Flash Loans and ERC1155 Multi Token Standard, with promising results.
Rateb Jabbar, Eya Dhib, Ahmed Ben Said, Moez Krichen ¡ 7 authors
The use of Blockchain technology has recently become widespread. It has emerged as an essential tool in various academic and industrial fields, such as healthcare, transportation, finance, cybersecurity, and supply chain management. It is regarded as a decentralized, trustworthy, secure, transparent, and immutable solution that innovates data sharing and management. This survey aims to provide a systematic review of Blockchain application to intelligent transportation systems in general and the Internet of Vehicles (IoV) in particular. The survey is divided into four main parts. First, the Blockchain technology including its opportunities, relative taxonomies, and applications is introduced; basic cryptography is also discussed. Next, the evolution of Blockchain is presented, starting from the primary phase of pre-Bitcoin (fundamentally characterized by classic cryptography systems), followed by the Blockchain 1.0 phase, (characterized by Bitcoin implementation and common consensus protocols), and finally, the Blockchain 2.0 phase (characterized by the implementation of smart contracts, Ethereum, and Hyperledger). We compared and identified the strengths and limitations of each of these implementations. Then, the state of the art of Blockchain-based IoV solutions (BIoV) is explored by referring to a large and trusted source database from the Scopus data bank. For a well-structured and clear discussion, the reviewed literature is classified according to the research direction and implemented IoV layer. Useful tables, statistics, and analysis are also presented. Finally, the open problems and future directions in BIoV research are summarized.
This paper investigates the market reaction to large positive or negative liquidity shocks on the value of tokens traded on decentralized exchanges (DEXes) on the Ethereum blockchain. Automated market makers (AMMs) and constant product markets provide transparent and decentralized ways to directly swap two blockchain tokens for each other via the use of liquidity pools. Using trade-by-trade data of 2.77 million swaps of 14 different tokens traded on Uniswap v2, v3 and SushiSwap, we find that the size of sell orders significantly correlates with negative future token returns, while buy size positively correlates with future token returns. Using an event study approach, we quantify the market reaction of unusually large sell and buy orders (top 1% percentile) and identify that the market reaction outweighs the economic value of the event by a factor of -7.4 for sell orders and +4.4 for buy orders over a short-span trading window. In the case of sell orders, a high proportion of the abnormal return is already realized before the event, which indicates informed trading in the form of arbitrage or frontrunning via Miner Extractable Value (MEV). Looking at individual crypto assets, we find a mean reassessment of token value following short sales of up to 0.79% within just one follow-up trade (buy orders up to 0.50%). The findings indicate that price shocks may have a signaling effect but also that market capitalization may be an insufficient metric for assessing the liquidity and valuation of (inefficient) crypto assets. The results suggest multiple challenges for investor protection in decentralized finance (DeFi) markets.
A smart contract is a computer program which is automatically executed with some conditional statements such as âif/thenâ. Since smart contracts can include some vulnerable program codes, smart contract exploit was recently highlighted as one of the severe threats to Ethereum blockchain. As one of the efficient and effective smart contract vulnerability detection methods, deep learning methods have been studied due to the fast detection speed and the high detection accuracy. Recently, the deep learning methods using convolutional neural network(CNN) have actively studied to classify images transformed from smart contracts into vulnerable or invulnerable. However, while simply transforming a smart contract into an image and analyzing, semantics and context of the smart contract are ignored to cause false detection alarms. To detect vulnerable smart contracts while maintaining their semantics and context, we propose a new code-targeted CNN architecture, called CodeNet. To improve the performance of CodeNet, we also design a data pre-processing procedure, where a smart contract is transformed into an image while maintaining locality. From the experimental results under various types of vulnerabilities, the proposed CodeNet-based vulnerability detection method shows the good-enough detection performance and detection time compared to well-known state-of-the-art vulnerability detection tools.
Leonardo H.S. Fernandes, Elie Bouri, JOSà W. L. SILVA, Lucian Bejan ¡ 5 authors
We examine the price disorder and market efficiency of five cryptocurrencies (Bitcoin, BNB, Cardano, Ethereum, and XRP) before and during COVID-19 pandemic period. Using permutation entropy and Fisher information measure (FIM), we construct the Shannon-Fisher causality plane (SFCP) to map these cryptocurrencies and their respective locations in a two-dimensional plane and then apply sliding time window approach to study the temporal evolution of efficiency. All cryptocurrencies exhibit high but slightly varying informational efficiency during both periods. Cardano is the most efficient. These results might point to the increasing maturity and lower potential for price predictability, which matter to cryp-tocurrencies usage for liquidity risk diversification strategy.
Francisco Javier GarcĂa-Corral, JosĂŠ Antonio Cordero-GarcĂa, Jaime de Pablo Valenciano, Juan Uribe-Toril
With the development of new technologies, some concepts become relevant in the economic area, as is the case with cryptocurrencies, in general, or Bitcoin and Ethereum, in particular. Due to the impact of these tools, a detailed bibliometric study that allows us to obtain all information about cryptocurrencies must be conducted. This study will help scientific production by specifying the development and lines of related research that have been followed and are currently being followed. We have used Tableau, R (Bibliometrix R Package), and VOSviewer software to analyze the information. These have been combined to create and review unified metadata from the Web of Science (WoS) and Scopus databases. The bibliometric analysis shows 771 articles on the WoS database and 648 articles on Scopus published between 2010 and early 2019. They present the most relevant articles, research areas, countries, institutions, authors, journals, and trends during the last few years. In conclusion, the number of publications has grown in the last 3 years. The analysis shows the evolution of blockchain technology used in this type of cryptocurrency. The review of this period marks a possible end to the historical part of cryptocurrencies, thereby opening the current topic to its multiple applications.
Cryptocurrencies and decentralized finance (DeFi) are reshaping how value is created, exchanged, and governed, and this chapter positions them as more than speculative instruments by reading them as an emerging financial infrastructure. In an ideal digital economy, programmable money supports low-friction transactions, broad participation, and transparent rules, while users retain control without surrendering trust to dominant intermediaries. Yet that ideal remains unevenly realized: markets still absorb extreme volatility, smart contracts still fail under adversarial conditions, and regulatory responses still oscillate between accommodation and restriction, leaving innovation and consumer protection in tension. Prior scholarship has mapped the monetary properties of Bitcoin as a scarcity-driven âdigital store of value,â and it has framed Ethereum as the computational base layer that makes smart contractsâand therefore DeFiâpossible. Studies on decentralized exchanges, lending protocols, automated market makers, and liquidity incentives have shown how 328 intermediated functions can be replicated through code, but they have also documented exploit patterns, oracle manipulation, governance capture, and composability risks that propagate across protocols. What remains underdeveloped is an integrated account that connects asset design, protocol architecture, and institutional constraints into a single explanatory model. To address this gap, the study advances a sociotechnical framework that links blockchain trust primitives with financial intermediation theory. By tracing how cryptocurrencies supply liquidity and collateral to DeFi, while DeFi amplifies token utility and systemic exposure, the chapter clarifies the conditions under which decentralized finance can mature into a resilient, inclusive financial ecosystem.
Yujia Tang, Chang Xu, Can Zhang, Yan Wu ¡ 5 authors
Abstract Tornado Cash, the most popular non-custodial coin mixer on Ethereum, is widely used to protect the privacy of addresses. However, some inappropriate transaction behaviors in Tornado Cash mixing mechanism lead to the risk of privacy leakage. More specifically, the malicious attackers can link multiple addresses of the same users according to the transaction data. Motivated by the above problem, this paper systematically analyzes the privacy issues of Tornado Cash for the first time. In this paper, we give the macroscopic analysis of Tornado Cash based on the on-chain data and formalize two types of transaction patterns. Focus on the presented transaction patterns, we propose three heuristic clustering rules to link the usersâ addresses, which reduce the size of usersâ anonymity set. Finally, we perform the experiment on real Tornado Cash transaction data to describe the effectiveness of the proposed clustering rules.
Web3 and DeFi are widely advocated as innovations for greater financial inclusion and democratization. We assemble the most comprehensive dataset to date on the largest Web3 ecosystem and use large-scale computing to investigate the claim. We discuss Ethereum's network structure, time trends, and distributions of transactions, mining, and ownership. Mining income and Ether ownership are concentrated in a few nodes, even after excluding exchange and mining pool wallets, with inequalities more exacerbated than observed in the real economy. Network activities are dominated by large transactions, shifting from peer-to-peer to user-DApps/DeFi interactions, and from Ether-based to ERC-20-token-based. High percentage transaction fees, congestion-induced gas-price fluctuation, suboptimal reserve setting, and large return volatility of tokens disproportionally harm small, unsophisticated, and new nodes, with high failure rates hurting all users. Finally, we present causal evidence that base-fee burning mechanisms (e.g., EIP-1559) and airdrop programs (e.g., OmiseGo Airdrop) promote inclusion and equality through monetary redistribution.
The majority of NFTs utilize the Ethereum blockchain platform to facilitate smart contracts. In this paper, we execute various econometric analyses to determine if this technical dependence induces a financial linkage to the risk, return, and prices of assets. For robustness, we also test the same relation between Bitcoin and NFTs. Empirical analyses are conducted through SADF bubbles test, DCC-GARCH time-varying correlation analysis, Bootstrap causality tests and spillover analysis. According to the results of various price, return, and volatility analyses, we find that NFTs do not demonstrate idiosyncratic features in their price developments and thus they cannot be considered as a separate asset class. Additionally, NFTs do not possess a specific financial linkage with Ethereum from using its infrastructure. Finally, we suggest NFT investors use alternative financial instruments, rather than Ether and Bitcoin in portfolio diversification, due to the presence of significant time-varying relationships and interactions.
Blockchain has widespread applications in the financial field but has also attracted increasing cybercrimes. Recently, phishing fraud has emerged as a major threat to blockchain security, calling for the development of effective regulatory strategies. Nowadays network science has been widely used in modeling Ethereum transaction data, further introducing the network representation learning technology to analyze the transaction patterns. In this paper, we consider phishing detection as a graph classification task and propose an end-to-end Phishing Detection Graph Neural Network framework (PDGNN). Specifically, we first construct a lightweight Ethereum transaction network and extract transaction subgraphs of collected phishing accounts. Then we propose an end-to-end detection model based on Chebyshev-GCN to precisely distinguish between normal and phishing accounts. Extensive experiments on five Ethereum datasets demonstrate that our PDGNN significantly outperforms general phishing detection methods and scales well in large transaction networks.
Arkan Hammoodi Hasan Kabla, Mohammed Anbar, Selvakumar Manickam, Taief Alaa Al-Amiedy ¡ 7 authors
Ethereum attracts more investors, researchers, and even scammers for many reasons; this is the first platform that enables the new Decentralized Applications (DApps) to run on top of the blockchain network. However, the rich semantics and applications of DApps inevitably introduce many security issues that have grabbed significant attention from industry and academics due to their destructive impact on DApps in recent years. Therefore, there is a vital need to study the applicability of Intrusion Detection System in detecting Ethereum-based attacks. Hence, this paper is among the first comprehensive review that studies the applicability of IDS in detecting Ethereum-based attacks. In addition, this paper lists all the potential attacks on Ethereum passing through the vulnerabilities that cause those attacks and ending with the consequences of each attack. Besides, this paper analyses all the IDS-based related works of Ethereum attacks detection since the Ethereum platform was launched in 2015. Finally, this paper discusses the open issues regarding vulnerabilities and attacks, challenges, and future directions.
There is a growing demand for transparency along the agri-food chain, both from customers and governments. The adoption of blockchain technology to enable secure traceability for the management of the agri-food chain, provide information such as the provenance of a food product and prevent food fraud, is emerging rapidly, due to the inherent trust and inalterability provided by this technology. However, developing the right smart contracts for these use cases is even more of a challenge than it is for those used in other fields. Several management systems for the agri-food chain based on blockchain technology and smart contract have been proposed, all however ad-hoc for a specific product or production process and difficult to generalize. In this paper, we propose a new approach to easily customize and compose general Ethereum-based smart contracts designed for the agri-food industrial domain, to be able to reuse the code and modules and automate the process to shorten development times, while keeping it safe and reliable. Starting from the definition of the real production process, we aim to automatically generate both the smart contracts to manage the system and the user interfaces to interact with them, thus producing a system that works semi-automatically. Additionally, we describe a honey production case study to show how our approach works. Future work will first extend the scope of the approach to other supply chains, furthermore, while the current platform used is Ethereum, in the future our approach will be easily extended to other blockchain platforms.
Arkan Hammoodi Hasan Kabla, Mohammed Anbar, Selvakumar Manickam, Shankar Karupayah
Recently, the rapid flourish of blockchain technology in the financial field has attracted many cybercriminalsâ attention to launch blockchain-based attacks such as Ponzi schemes, Scam wallets, and phishing scams. Currently, Ethereum is the most prominent blockchain-based platform and the first that supports smart contracts. However, the number of phishing scam accounts are reportedly more than 50% of all cybercrimes in Ethereum. In contrast, this paper proposes a detection mechanism called Ethereum Phishing Scam Detection (Eth-PSD) that attempts to detect phishing scam-related transactions using a novel machine learning-based approach. Eth-PSD tackles some of the limitations in the existing works, such as the use of imbalanced datasets, complex feature engineering, and lower detection accuracy. We also investigated the aspects of constructing a new updated and balanced dataset that can be used for evaluating Eth-PSD effectively. Our experimental results indicate that Eth-PSD could efficiently detect the phishing scam on Ethereum with a detection accuracy of 98.11%, with a very low False Positive Rate of 0.01. Taken together, Eth-PSD showed a superior advantage compared to the existing works in reducing the dimensionality of the dataset by feature engineering and achieved an overall detection accuracy with an improvement of at least 6% compared to other existing solutions from the related work.
Blockchain technology has the characteristics of decentralization, traceability and tamper-proof, which creates a reliable decentralized trust mechanism, further accelerating the development of blockchain finance. However, the anonymization of blockchain hinders market regulation, resulting in increasing illegal activities such as money laundering, gambling and phishing fraud on blockchain financial platforms. Thus, financial security has become a top priority in the blockchain ecosystem, calling for effective market regulation. In this paper, we consider identifying Ethereum accounts from a graph classification perspective, and propose an end-to-end graph neural network framework named Ethident, to characterize the behavior patterns of accounts and further achieve account de-anonymization. Specifically, we first construct an Account Interaction Graph (AIG) using raw Ethereum data. Then we design a hierarchical graph attention encoder named HGATE as the backbone of our framework, which can effectively characterize the node-level account features and subgraph-level behavior patterns. For alleviating account label scarcity, we further introduce contrastive self-supervision mechanism as regularization to jointly train our framework. Comprehensive experiments on Ethereum datasets demonstrate that our framework achieves superior performance in account identification, yielding 1.13% ~ 4.93% relative improvement over previous state-of-the-art. Furthermore, detailed analyses illustrate the effectiveness of Ethident in identifying and understanding the behavior of known participants in Ethereum (e.g. exchanges, miners, etc.), as well as that of the lawbreakers (e.g. phishing scammers, hackers, etc.), which may aid in risk assessment and market regulation.
On September 15, 2022, the Ethereum network adopted a proof-of-stake (PoS) consensus mechanism. We study the impact on the network and competing platforms in a two month event window around the Beacon chain merge. We find that the transition to PoS has reduced energy consumption by 99.98%. Miners have not transformed into validators, and total block reward income (in USD) has fallen by 97%, though transaction fees (in ETH) for Ether have increased nearly 10%. The Herfindahl index for the top 10 is 1,009; the network is 19% less concentrated after the merge. Ethereum supply growth has been deflationary since the merge. The time between consecutive blocks is now steady at 12 seconds and transactions per day are up 7.0%. On Polygon, Matic fees rose but token fees fell. Polygon also slows, processing 3.3% fewer transactions per day. Solana's fees fall by $0.0003, and transactions per day are down 48%. Stablecoin transfer volumes fall on Ethereum and Polygon, but rise on Solana.