In recent years, the Ethereum blockchain has seen significant growth and adoption.One of the key factors of its success is the possibility to run immutable programs known as smart contracts.Smart contracts allow for the automatic manipulation of digital assets and play a central role in the new decentralized finance (DeFi) ecosystem.With the growth of DeFi, the interactions between smart contracts have become increasingly complex, enabling advanced financial protocols and applications.However, bugs in smart contract interactions are also a common cause of critical vulnerabilities that result in considerable financial losses.In this paper, we study and detect a type of cross-contract vulnerability known as a storage collision.A smart contract uses storage to persistently store its data on the blockchain.Typically, each contract has its own separate storage.However, it is also possible that two smart contracts share their storage (using a delegate call).Unfortunately, when these two contracts have different understandings of the types/semantics of their shared storage, a storage collision vulnerability can occur.This may lead to unexpected behavior such as denial of service (frozen funds), privilege escalation, and theft of financial assets.To detect and investigate the impact of storage collision vulnerabilities at scale, we propose CRUSH, a novel analysis system that discovers these flaws and synthesizes proof-of-concept exploits.We leverage CRUSH to perform a large-scale analysis of 14,237,696 smart contracts deployed on the Ethereum blockchain since its genesis.CRUSH identifies 14,891 potentially vulnerable contracts and automatically synthesizes an end-to-end exploit for 956 of them.Our system uncovers more than $6 million of novel, previously unreported potential financial damage caused by storage collision vulnerabilities.
The Internet of Healthcare Things (IoHT) is an emerging critical technology for managing patients’ health. They are prone to cybersecurity vulnerabilities because they are connected to the internet, primarily by wireless connections. This is a major concern, considering data privacy and security. Artificial intelligence (AI) models are excellent methods to detect and mitigate cybersecurity vulnerabilities. Since medical Information Technology (IT) is evolving and data privacy is a major concern with sensors generally, in healthcare IoT. The TON_IOT, Edge_IIoT, and UNSW-NB15 datasets were used in this study for assessment and implementation to solve the challenge using the chosen benchmark AI models with the integration of IPFS blockchain technology in order to decentralize and secure the data. Justifiable parameters were used to determine how efficient each technique is in predicting the best outcome. The results show the efficiency of the utilized models, particularly the Support Vector Machines (SVM). The TON_IoT dataset obtained 100% accuracy, the Edge_IIoT dataset obtained 98% accuracy, and the UNSW-NB15 dataset obtained 89% accuracy. The integrated blockchain technology in this model is applied for security purposes. Utilizing these techniques will proffer a secure and safe transmission of medical data. This study will generally provide important insight to other researchers in the healthcare field.
Kose John, Barnabé Monnot, Peter Mueller, Fahad Saleh · 5 authors
We provide comprehensive background regarding the Ethereum blockchain protocol, focusing especially on the economic incentives of participants. We begin by clarifying the transaction life-cycle from the user perspective, explaining how user transactions are submitted and settled on the blockchain. Thereafter, we explain how the Ethereum protocol selects proposers to propose blocks of transactions for inclusion on the blockchain and also attesters to vote for or against those blocks. We discuss both how the Proof-of-Stake protocol is used to select proposers and attesters, and also how the Gasper protocol is used to aggregate attester votes which thereby determine the finalized blockchain. Finally, we discuss how builders, searchers and relays have arisen to support and enhance the Ethereum block production process. Through our discussion, we clarify the economic trade-offs faced by each participant and the associated real-world decision variables for each participant.
Nebojša Horvat, Dušan Gajić, Petar Trifunović, Veljko Petrović · 6 authors
Distributed ledger technology (DLT) and blockchain-based energy trading solutions are often labeled as resource-hungry, excessive power consumers which consequently create a significant carbon footprint. Using this approach to energy trading, without simultaneously minimizing energy consumption, defeats the purpose of using such trading solutions to encourage renewable energy production and may even cancel out any ecological benefits. This paper demonstrates that it is possible to create a DLT-based energy trading system which provides security, transparency, autonomy, scalability, and decentralization, provided by using a DLT, but with significantly lower penalties than other previously known solutions. This is best illustrated through the fact that the carbon footprint per transaction of the solution presented in this paper is 0.19 grams of$CO_{2}$compared to 20 grams of$CO_{2}$created by single transaction of similar complexity on the Ethereum blockchain. This makes per-transaction carbon footprint of our system approximate 100 times smaller than that of the most commonly used Ethereum blockchain. We present the architecture and features of the proposed platform, as well as a thorough analysis of its performance, including power consumption and estimated carbon footprint. All experiments are done on a dedicated Beowulf cluster comprised of general-purpose computers. The cluster mimics a microgrid environment and presents a testing ground for real-world performance and power consumption analysis of a system used for trading energy predominantly produced from prosumers and their renewable sources.
Cryptocurrencies experienced a huge surge whose value reached more than US $ 191 million or Rp. 2.7 trillion. Interestingly, almost all types of cryptocurrencies do not have an underlying asset as a common underlying asset in ordinary investments. Bitcoin and Ethereum claims that its underlying asset is the coin miner charges from the amount of hardware and electricity used in the transaction. Tether and USDC claim that their underlying assets are in US dollars. This article examines Islamic law regarding the underlying assets in the form of coin mining fees and US Dollars. The questions that arise are, how is the study of Islamic law regarding the underlying asset in the form of coin mining fees and US Dollars? Furthermore, the ideal pattern of a cryptocurrency scheme that includes assets in the form of tangible goods refers to manafiul a’yan? This research uses the gate of legal philosophy approach, looks at the business scheme in terms of values and principles and then provides legal conclusions based on that assessment. From the research conducted, first, the underlying asset of coin mining costs cannot be said to be an underlying asset that is truly economically useful for coin owners, except for the technology access costs which are clearly experienced by all technologies. Second, the underlying asset in the form of US Dollars has clearer benefits, but this is contrary to Islamic law. Third, for the underlying asset in the form of tangible goods, ownership must always be included in every coin purchased.
Currently cryptocurrencies and Decentralized Finance (DeFi), which enable financial services on public blockchains, represents a new growing trend in finance. In contrast to financial markets, ruled by traditional corporations, DeFi is completely transparent as it keeps records of all transactions that occur in the network and makes them publicly available. The availability of the data represents an opportunity to analyze and understand the market from the complexity that emerges from the interactions of the actors (users, bots and companies) operating in the embedded market. In this paper we focus on the Ethereum network and our main goal is to show that the properties of the underlying transaction network provide further and useful information to forecast the evolution of the market. We aim to separate the non redundant effects of the blockchain transaction network properties from classic technical indicators and social media trends in the future price of Ethereum. To this end, we build two machine learning models to predict the future trend of the market. The first one serves as a base model and considers a set of the most relevant features according to the current scientific literature including technical indicators and social media trends. The second model considers the features of the base model, together with the network properties computed from the transaction networks. We found that the full model outperforms the base model and can anticipate 46 more rises in the price than the base model and 19 more falls.
In this paper, we follow the position of art, artists and works of art with regard to the conditions in which the work of art is created, in an environment ruled by capitalist relations and the high-tech environment that accompanies them. The transition from analog to digital formats has opened new perspectives for artistic creation and distribution of works of art, but also problems related to copyright protection and fair monetization of works of art. The advent of blockchain technology, especially the Ethereum blockchain platform with open source technology and smart contracts, has enabled more efficient communication, distribution and monetization of artwork. The focus of this paper is especially narrowed on the emergence of NFT (Non Fungible Tokens) that can be carriers of the value of a work of art. They enable the uniqueness of the work to be maintained through controlled scarcity. We will present the ERC-721 standard, which enables the creation of NFT, as well as the first five leading platforms for placing NFT tokens on the blockchain through selected examples.
In this study, we examine the potential benefits and difficulties of integrating blockchain technology based on Ethereum into logistics management systems. Our goal is to offer a thorough grasp of this technology's influence on the logistics sector by looking at its theoretical underpinnings and real-world implementations. Significant outcomes from our research include greater logistic transparency, real-time updates, increased security, and automation of contractual duties. These findings underscore the need to embrace innovation and create a legislative framework that facilitates the implementation of blockchain technology, with broad ramifications for logistics firms and legislators. Our study adds to the expanding corpus of information on the application of blockchain technology in logistics, offering insightful information to scholars, policymakers, and business professionals.
The World Health Organization (WHO) announced the Covid-19 pandemic in March 2020, which had a negative impact on economic activities and financial markets. Cryptocurrencies with blockchain technology, whose history is not old, took off in the Covid-19 period thanks to digital transformation and became popular in the financial markets. However, the fact that cryptocurrencies lose blood after the pandemic period. This study examines the volatility of cryptocurrencies before, during and after the pandemic Covid-19 using data from 4 cryptocurrencies (Bitcoin, Ethereum, Binance and Litecoin) and the CCI30 index, using autoregressive conditional variance models with two dummy variables. According to the results, the volatility of cryptocurrencies decreases throughout the pandemic period, moreover, decreases more after the pandemic compared to the pre-pandemic period. Investors should be cautious about investing in these risky instruments, which may become popular again in the future, just in case.
Fraudulent activity detection within blockchain networks has become a critical concern due to the widespread adoption of decentralized technologies in financial and digital systems. The paper introduces a system that uses Blockchain and Machine Learning (ML)to strengthen the security of banks. Employing the services of the Ethereum blockchain dataset, the model applies a comprehensive methodology involving data preprocessing, feature engineering, Z-score normalization, and stratified data splitting. Genetic Algorithm-optimized Support Vector Machine (GA-SVM) and Artificial Neural Network (ANN) are constructed and tested, and their results are then compared with those from Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Convolutional Neural Network (CNN) models. Metrics of accuracy by using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) as measures. It was found that the GA-SVM model achieved the best results compared to other models, with MAE at 0.1032 and MAPE at 4.6938 on test data, which confirms its usefulness in real-time fraud detection. When the model connects with smart contracts, it helps prevent fraudulent activities and supports both transparency and good operations in blockchain-based finance.
With rising concerns about the security of IoT devices, network operators need better ways to handle potential risks. Luckily, IoT devices show consistent patterns in how they communicate. But despite previous efforts, it remains unclear how knowledge of these patterns can be made available. As data marketplaces become popular in different domains, this paper1 proposes creating a special marketplace focused on IoT cybersecurity. The goal is to openly share knowledge about IoT devices' behavior, using structured data formats like Manufacturer Usage Description (MUD) files. To make this work, we employ technologies like blockchain and smart contracts to build a practical and secure foundation for sharing and accessing important information about how IoT devices should behave on the network. Our contributions are two-fold. (1) We identify the essential features of an effective marketplace for sharing data related to the expected behaviors of IoT devices. We develop a smart contract on the Ethereum blockchain with five concrete functions; and, (2) We implement a prototype of our marketplace in a private chain environment-our codes are publicly released. We demonstrate how effectively our marketplace functions through experiments involving MUD files from consumer IoT devices. Our marketplace enables suppliers and consumers to share MUD data on the Ethereum blockchain for under a hundred dollars, promoting accessibility and participation.
Blockchain architecture is based on distributed and decentralised technology used to store transaction records in blocks [1].These blocks are linked to each other based on the value of the hash address (previous hash) generated through a cryptographic mechanism [2].Blockchain technology has developed as an open ledger to record transactions in a distributed manner.New blocks will be created after the mining process is complete through the protocol consensus that requires each peer to verify transactions [3][4][5].
This study emphasizes the implication of dynamic connection between digital currency and Nigerian economic growth rate by focusing attention on Bitcoin, Ethereum and Litecoin with respect to their returns and volatility from 2010Q4 to 2022Q3. As a way to have a robust estimation, we model our analysis using ARDL model and granger causality test. This model is rather useful to have both short and long run estimations. Importantly the study’s outcome conforms with the fundamentals. By findings from the study, the trend analysis suggests that the country’s exchange rate moves in line with digital currency activities while at the same time signifies some implication on the growth rate of the Nigerian economy. While lower returns for Bitcoin and Litecoin increase growth rate, the return for Ethereum rather move in the same direction as the growth rate. This indeed suggest that most Nigerians into digital currency activities often engage in portfolio diversification among available coins. The study further found that low volatility in the market will raise (significantly especially for Ethereum) growth rate of the economy while causal implication run from returns and volatilities of these coins to growth and exchange rates. Indeed, the findings have important policy implication for the Nigerian economy which suggests paying good attention to digital currency activities in the country and formulating necessary policies to improve it.
<p>Cryptocurrencies represent a new form of property that exists only on the Internet. The first cryptocurrency to appear on the global market was Bitcoin, and its appearance is linked to the first financial crisis in 2008. The market capitalization of Bitcoin grew very quickly and in 2017 reached the highest capitalization in history (334 billion US dollars). Later, there was an expansion of new cryptocurrencies, of which there are currently over 1500 (e.g. Ethereum, Tether, BNB, USD Coin, etc.). The emergence of cryptocurrencies as a new concept affects the change in the perception of payments and money as a means of payment in general. The development of the global cryptocurrency market is indeed rapid and dynamic, and it is becoming an increasingly popular method of payment. Since the emergence of cryptocurrencies is not related to central banks, the need to change the classical systems and economic policies of countries is also expressed. The dynamic development of the mentioned market takes place in parallel with the development of information technologies, so there is a trend of capital outflow from classic capital markets to emerging global cryptocurrency markets. The question arises of the survival of traditional banks in the future, as more users trust cryptocurrencies. Global cryptocurrency markets are expected to expand more and more in the future. The paper will analyze the development of the global cryptocurrency market based on a sample of five of the most significant cryptocurrencies today.</p>
This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and Ethereum Dominance (ETH.D) in a daily timeframe. The methods used to teach the data are hybrid and backpropagation algorithms, as well as grid partition, subtractive clustering, and Fuzzy C-means clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed in this paper has been compared with different inputs and neural network models in terms of statistical evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time.
As a consequence of rising geo-economic issues, global currency values have declined during the last two years, stock markets have performed poorly, and investors have lost money. Consequently, there is a renewed interest in digital currencies. Cryptocurrency is a fresh kind of asset that has evolved as a result of fintech innovations, and it has provided a major research opportunity. Due to price fluctuation and dynamism, anticipating the price of cryptocurrencies is difficult. There are hundreds of cryptocurrencies in circulation around the world and the demand to use a prediction system for price forecasting has increased manifold. Hence, many developers have proposed machine learning algorithms for price forecasting. Machine learning is fast evolving, with several theoretical advances and applications in a variety of domains. This study proposes the use of three supervised machine learning methods, namely linear regression, support vector machine, and decision tree, to estimate the price of four prominent cryptocurrencies: Bitcoin, Ethereum, Dogecoin, and Bitcoin Cash. The purpose of this study is to compute and compare the precision of all three techniques over all four datasets.
В статті представлений алгоритм та реалізацію моніторингової системи, що дозволяє повідомляти користувача про підозрілу активність зі смарт контрактом проєкту, в який користувач інвестує децентралізовані активи. Користувачі зможуть формувати запити на моніторинг транзакцій з конкретних гаманців та на конкретні протоколи, при цьому застосовуючи фільтри на параметри виклику методів смарт контракту. За рахунок точного алгоритму моніторингу та якісного блокчейн провайдера між проміжок часу між виявленням підозрілої транзакції та сповіщенням є мінімальним та не має затримок. Під час розробки наукової статті було проаналізовано існуючі безпекові рішення та підходи для збереження користувацьких активів залучених до блокчейн протоколів. Окрім існуючих рішень було досліджено статистику та конкретні підходи зламів протоколів, а також шахрайські проєкти, які були помічені на викраданні проінвестованих користувацьких активів. Проаналізовано конкретні кейси шахрайства включаючи конкретні типи транзакцій, а саме транзакції на вивід ліквідності з децентралізованих бірж, а також маніпуляції з передачі права власності на протокол на інший аккаунт чи зупинка протоколу. Розроблена система може бути застосована на всіх блокчейнах, що побудовані на основі EVM – Ethereum Virtual Machine, а саме Ethereum, Binance Smart Chain, Polygon тощо, що дозволить підвищити загальний рівень безпеки користувацьких активів, а також проінформувати користувачів про можливі загрози при взаємодії з новими невідомими протоколами. Важливим моментом є оновлення та додавання нових типів шкідливих транзакцій до системи моніторингу для забезпечення запобігання більшій кількості випадків шахрайства. На основі спроектованої системи можуть бути розроблені автоматизовані системи, що автоматично відправляють користувацьку транзакцію на протидію виявленій підозрілій транзакції.
Yi-Jen Su, Chao-Ho Chen, Tsong-Yi Chen, Chun-Wei Yeah
The main causes for the risks of used-car trading lie in the information asymmetry between buyers and sellers and the absence of a trust mechanism. This study proposed applying the Ethereum blockchain and InterPlanetary File System (IPFS) to construct a used-car trading and management information system that supports decentralized data storage services. This mechanism could support the permanent storage, immutability, and traceability of car maintenance data through the operation of smart contracts. Furthermore, car information is stored and managed by IPFS. Slither monitors the security of this system by detecting security bugs in the operation of smart contracts.
Christiana Chamon, Kamalesh Mohanasundar, Sarah A. Flanery, Francis K. Quek
This paper presents a statistical physical generation of random keys for a decentralized identity ecosystem that uses Web 3.0 protocols. Web 3.0 is driven by secure keys, typically represented in hexadecimal, that are pseudo-randomly generated by an initialization vector and complex computational algorithms. We demonstrate that the statistical physical Kirchhoff-law-Johnson-noise (KLJN) scheme eliminates the additional computational power by naturally generating truly random binary keys to drive the creation of decentralized identifiers (DIDs) that are appended to an Ethereum blockchain.
Mustafa Kamal, Sabir Ali Siddiqui, Nayabuddin, Afaf Alrashidi · 11 authors
The study and investigation of the behavior of monetary phenomena is an interesting subject for actuaries and practitioners. In the recent age and development in the monetary and financial phenomena, cryptocurrency has gained much attention from actuaries. Over the past decade, several research studies have emerged on modeling and forecasting cryptocurrency exchange rates. This paper also contributes to the modeling of cryptocurrency exchange rates using a new version of the Logistic distribution, namely, a new cotangent-Logistic distribution. The mathematical properties and estimators of the new cotangent-logistic distribution's parameters are obtained. We illustrate the new cotangent-Logistic distribution using two financial data sets representing the log-returns of the Bitcoin and Ethereum prices. We compare the new cotangent-Logistic distribution with the baseline Logistic distribution and its modified version. Using the p-value and three other statistical tests, we show that the new cotangent-Logistic distribution repeatedly provides the optimal fit to cryptocurrency exchange rates.
Wash trading in decentralized markets remains a significant concern magnified by the pseudonymous and public nature of blockchains. In this paper we introduce an innovative methodology designed to detect wash trading activities beyond surface-level transactions. Our approach integrates NFT ownership traces with the Ethereum Transaction Network, encompassing the complete historical record of all Ethereum account normal transactions. By analyzing both networks, our method offers a notable advancement over techniques proposed by existing research. We analyzed the wash trading activity of 7 notable NFT collections. Our results show that wash trading in unregulated NFT markets is an underestimated concern and is much more widespread both in terms of frequency as well as volume. Excluding the Meebits collection, which emerged as an outlier, we found that wash trading constituted up to 25% of the total trading volume. Specifically, for the Meebits collection, a staggering 93% of its total trade volume was attributed to wash trading.
Vignesh Ramamoorthy H, Spelmen Vimalraj Santhanam, V. Vibithrapriya, R. G. Harshini
Nowadays the quest for electronic payments has created a huge ambit among academicians and businesspeople. At the same time, transactions are repressed because of the intervention of third parties. To overcome this situation, the great as well as the puzzling imposter arose which is now the area of interest called cryptocurrency. Bitcoins, Ethereum, and ripple are some embodiments of cryptocurrency. Investors do not always have a bed of roses with cryptocurrency as the frequent oscillation of prices is hard to forecast. The paper here deals with the forecasting of cryptocurrency prices by using data mining algorithms such as Bagging, K-NN, Linear Regression, and Support Vector Machine. The outcome specifies the accuracy value gained from the cryptocurrency forecasting model from which we can predict the price of the cryptocurrency.
Aminu Adamu Ahmed, Isah Muhammad Alhassan, Ahmadi Abubakar Majidadi, Mohammed Nura Musa
Cryptocurrency has become a disruptive force in the twenty-first century's quickly changing world, completely changing how we view and interact with conventional financial institutions. This in-depth analysis intends to give insight on the underlying technology, economic ramifications, and legal difficulties that have formed this digital phenomena in order to examine the origins, development, and effect of cryptocurrencies over the past two decades. This article begins by exploring the history of cryptocurrencies, tracing its origins back to the mysterious Satoshi Nakamoto and the invention of Bitcoin in 2009. It analyses the underlying blockchain technology that underpins cryptocurrencies, giving readers a thorough understanding of its decentralised nature and the potential it has to revolutionise a number of industries outside of banking. The study also looks at the wide variety of cryptocurrencies that have appeared since the popularity of Bitcoin, including Litecoin, Ethereum, and Ripple, among countless others. It explores their distinctive characteristics, use cases, and market dynamics, stressing their potential for financial inclusion, international trade, and smart contract applications. The economic effects of cryptocurrencies are also examined in this paper, along with how they affect decentralised finance (DeFi), traditional banking systems, and financial innovation. It also looks at the difficulties presented by cryptocurrencies, including their volatility, scalability, and security issues, as well as the ongoing discussions surrounding their regulation and widespread use
The study explores the spillover effect on Ethereum – one of the leading cryptocurrencies – stemming from key variables in the domains of cryptocurrencies, investor sentiment, and traditional financial markets. This paper is the first to analyze the influence of such dominant representatives from diverse, external fields on cryptocurrency. We select bitcoin, the Fear and Greed index, the Standard and Poor’s 500 index and the United States Dollar to Euro Exchange Rate as representatives to investigate the spillover effect on Ethereum. Utilizing linear regression models and vector autoregressive (VAR) models, we find strong correlations between Ethereum’s return and that of Bitcoin’s, along with investor sentiment. However, the influence of financial market variables on Ethereum are found to be virtually static and negligible. This research offers valuable insights to those seeking to forecast or manipulate crypto market movement through analyzing the complex interplay between these variables and Ethereum.