We study the determinants of transaction fees in the Bitcoin and Ethereum blockchains, particularly focusing on the recent Merge – Ethereum's transition from a Proof-of-Work to a Proof-of-Stake consensus mechanism. Using multivariate regression analysis, we find that blockchain fees in USD increase with network congestion, measured by the mempool count, in a non-linear manner as hypothesized by Huberman, Leshno, and Moallemi (2021). Additionally, the fees are positively influenced by the respective native currency's returns over the past six months. The Merge resulted in a fee reduction for both Bitcoin and Ethereum blockchains.
This study examines the price dynamics of Bitcoin and Ethereum between 2013 and 2022 using two distinct approaches: financial market technical analysis and econometric analysis. Financial market technical analysis employs indicators such as the Relative Strength Index (RSI) and the Hull moving average, while econometric analysis involves the Hodrick-Prescott filter and an Autoregressive Distributed Lag (ARDL) model. The study shows that Bitcoin and Ethereum experienced supercycle years in 2013, 2017, and 2021. The Bitcoin cycle, which averages 3.5 years, was particularly emphasized. The impact of the Bitcoin halving is also noteworthy, especially in the formation of supercycle bubbles in 2021, which affected altcoins such as Ethereum. The implications of this extend to portfolio management advice. It is recommended to carefully evaluate portfolio diversification and adopt a proactive regulatory approach, especially during the Bitcoin halving period.
Lars Hornuf, Paul P. Momtaz, Rachel J. Nam, Ye Yuan
We examine how cybercrime impacts victims’ risk-taking and returns. Our difference-indifferences analysis of a sample of victims and matched non-victims is in line with prospect theory and suggests that victims increase their long-term total risk-taking after losing part of their wealth. Victims also earn lower risk-adjusted returns in the post-cybercrime period. Victims’ long-term total risk-taking increases because they increase diversifiable risk in the long term. The increased diversifiable risk correlates with victims’ withdrawal from altcoins after cybercrime. At the same time, the reduction in risk-adjusted returns correlates with increased trading activity and churn, due plausibly to managing cybercrime exposure. In the cross-section of Ethereum addresses, we show that the most affluent victims take a systematic approach to restore their pre-cybercrime wealth level, while the least affluent victims turn into gamblers. Finally, a parsimonious forensic model explains a good part of the addresses’ probability of being involved in cybercrime, on both the victim and the cybercriminal side.
Numerous abnormal transactions have been exposed as a result of targeted attacks on Ethereum, such as the Ethereum Decentralized Autonomous Organization attack. Exploiting vulnerabilities in smart contracts, malicious users can pursue their own illicit objectives through abnormal transactions. Consequently, identifying these malevolent users, implicated in fraudulent activities and their attribution, becomes exceedingly complex. Cryptocurrency transactions used for malicious purposes, employing pseudo-anonymous accounts to send and receive ransom payments and accumulating funds under various identities, further highlight the need to control and detect these abnormal transactions for maintaining a high level of security within the Ethereum network. Although existing Intrusion Detection Systems (IDSs) help mitigate abnormal transaction occurrences, their performance necessitates improvement. To address this issue, this study presents a novel approach, named Abnormal Transactions Detection Using a Semi-Supervised Generative Adversarial Network (ATD-SGAN), which efficiently detects abnormal attacks within the Ethereum network. ATD-SGAN leverages a semi-supervised generative adversarial network for this purpose. The results demonstrate that ATD-SGAN significantly enhances the performance of state-of-the-art IDSs. It achieves an increase in detection accuracy from 3.78% to 11.05% and reduces the false alarm rate from 42.29% to 0.15%. Moreover, ATD-SGAN notably improves the F1-measure, ranging from 10.39% to 3.79%, compared to the current IDSs.
Before the advent of alternative blockchains such as Ethereum, the future of decentralization was all in the hands of Bitcoin. Together with Nakamoto itself, early developers were trying to leverage Bitcoin’s potential to decentralize traditionally centralized applications. However, because Bitcoin was a decentralized machine, the available non-trustless oracles were considered unsuitable. Therefore, strategies had to be elaborated to solve the so-called “oracle problem” in the newborn scenario. By interviewing early developers and crawling early forums and repositories, this paper aims to retrace and reconstruct the chain of events and contributions that gave birth to oracles on Bitcoin. The evolution of early protocols, along with the difficulties encountered in their development, are also outlined. Analyzing technical and social barriers to building oracles on Bitcoin, the transition to Ethereum will also be discussed.
Ethereum is a permissionless blockchain ecosystem that supports execution of smart contracts, the key enablers of decentralized finance (DeFi) and non-fungible tokens (NFT). However, the expressiveness of Ethereum smart contracts is a double-edged sword: while it enables blockchain programmability, it also introduces security vulnerabilities, i.e., the exploitable discrepancies between expected and actual behaviors of the contract code. To address these discrepancies and increase the vulnerability coverage, we propose a new smart contract security testing approach called transaction encapsulation. The core idea lies in the local execution of transactions on a fully-synchronized yet isolated Ethereum node, which creates a preview of outcomes of transaction sequences on the current state of blockchain. This approach poses a critical technical challenge — the well-known time-of-check/time-of-use (TOCTOU) problem, i.e., the assurance that the final transactions will exhibit the same execution paths as the encapsulated test transactions. In this work, we determine the exact conditions for guaranteed execution path replicability of the tested transactions. To demonstrate the transaction encapsulation, we implement a transaction testing tool, TxT, which reveals the actual outcomes (either benign or malicious) of Ethereum transactions. To ensure the correctness of testing, TxT deterministically verifies whether a given sequence of transactions ensues an identical execution path on the current state of blockchain. We analyze over 1.3 billion Ethereum transactions and determine that 96.5% of them can be verified by TxT. We further show that TxT successfully reveals the suspicious behaviors associated with 31 out of 37 vulnerabilities (83.8% coverage) in the smart contract weakness classification (SWC) registry. In comparison, the vulnerability coverage of all the existing defense approaches combined only reaches 40.5%.
Yusuf Ucbas, Amna Eleyan, Mohammad Hammoudeh, Manar Alohaly
Blockchain emerged in the last decade as a promising technology with possible applications in numerous fields such as healthcare, supply chain, and finance. Its immutability, transparency, security, and decentralisation gained significant attention in academia and industry. One technology that blockchain can support is the Internet of Things (IoT). However, there are still challenges hindering the real-world adoption of blockchain due to concerns about its performance, scalability, and complexity. This study contributes to a comparative study that analyses blockchain platforms in terms of their performance and scalability, with specific reference to IoT applications. We focus on the Ethereum and Hyperledger Fabric blockchain platforms. As part of the implementation, an IoT healthcare use case is developed. We conducted performance and scalability tests on private platform networks to measure the throughput and latency parameters. To evaluate scalability, we examined the behaviour of the studied platforms in response to an increase in the number and rate of transactions. Hyperledger Caliper is used to collect these parameters. Experiment analysis shows that Fabric outperforms Ethereum in terms of latency and throughput. As for performance and scalability analysis, Fabric was found to be more suitable than Ethereum for private networks such as IoT healthcare ecosystems.
Faiqah Hafidzah Halim, Nor Aimuni Md Rashid, Nur Farahin Mohd Johari, Muhammad Amirul Hazim Abdul Rahman
In Malaysia, private healthcare providers keep computerized records of vaccination data, including personal information, diagnostic results, and vaccine prescriptions. However, such sensitive information is commonly stored using a centralized storage paradigm which subsequently brings about the issue of maintaining user privacy. Concerning this, unauthorized access to crucial information such as identity details and ailments that a patient is suffering from, as well as the misuse of patients' data and medical reports, are common threats to user's (patient) privacy. To overcome this problem, the researchers suggest leveraging IPFS (Interplanetary File System) and blockchain technology to create a decentralized children's immunization record management system. While respecting patient privacy, the proposed system also allows authorized entities, such as healthcare professionals, and provides easy access to medical data (e.g., doctors and nurses). The proposed decentralized system integrates IPFS, blockchain, and AES cryptography to ensure consistency, integrity, and accessibility. A permission Ethereum blockchain allows hospitals and patients within private healthcare providers to connect. We utilized a combination of symmetric and asymmetric key encryption to provide secure storage and selective records access. The proposed system was analyzed using Wireshark to evaluate the overall system's performance in terms of integrity and accessibility while sharing patient records. This project aims to provide automated system keeper using autonomous agents collaboratively with the role of blockchain for further enhancement.
Unlike general passwords, user authentication technology using biometric data cannot be lost or forgotten, and it has the advantage of being impossible to forge or falsify by attackers. Since this biometric data contains sensitive information, a safe storage method is needed. However, if biometric data is managed on a central server, it is limited by being vulnerable to integrity infringement attacks by system attackers and persistent infringement attacks on the authentication service. To solve this problem, a model using blockchain-based information security technology is proposed in this paper. The aim is to safely manage the user biometric data by dispersing the storage of the biometric data feature information for user authentication in smart contracts and IPFS using Ethereum. We have verified that it takes an average of 1,472.733 ms and 217.829 ms, to register and authenticate a user in the proposed model and we expect that it will provide a reliable authentication service to the users compared to the existing authentication method in which the biometric data is managed by a single server.
With the advent of 2022, the impact of the COVID-19 pandemic has weakened, the US labor market has recovered, and inflation has been severe, creating the conditions for the Fed to tighten its policies. At the same time, cryptocurrencies as a hot topic in recent years; ETH is one of the most popular cryptocurrencies in the market; this article aims to assess the impact of the Fed's raised interest rates on the yield and volatility of cryptocurrency Ethereum (ETH) based on data on the ETH price and the US dollar/CNY exchange rate since 2022. And further, simulate the impact on the overall cryptocurrency market. This paper constructs VAR and ARMA-GARCH models to analyze ETH returns and volatility variations. The results of these models suggest that the exchange rate rise triggered by the Fed's rate hike has had a negative impact on ETH yields and increased the volatility of its returns. Further, this article recommends that investors should adjust their portfolios according to their risk appetite in an uncertain market environment.
In recent decades, data analytics has become increasingly involved in people's daily lives. Machine learning, an important part of data analysis, has also been used in the financial sector. Contemporarily, the high volatility feature of cryptocurrencies has attracted lots of investors, which also brings lots of difficulty to predict and analyze. In fact, the price of cryptocurrencies can also be forecasted based on machine learning. This paper uses historical data of Bitcoin, Ethereum and Dogecoin as inputs to predict the future value based on the LSTM. LSTM model can learn the long-term dependencies in data. According to the analysis, mean absolute error calculate the average size of the error in a set of predictions, regardless of its direction. The results produced can roughly predict the future trends of these three cryptocurrencies. This paper combines the fields of machine learning and finance to predict the future value of cryptocurrencies. These results shed light on guiding further exploration of predicting cryptocurrency assets valuation based on LSTM model.
The study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. The FB Prophet time series model and the LSTM recurrent neural network were selected to implement the study. Using the example of data from Binance (the most popular exchange in Ukraine) for the period from 06.07.2020 to 01.04.2023, prices for Bitcoin, Ethereum, Ripple, and Dogecoin were modeled and forecasted. The recurrent neural network of long-term memory showed significantly better results in forecasting according to the RMSE, MAE, and MAPE criteria, compared to the Naïve model, the traditional ARIMA model, and the FB Prophet results.
This article compares hardware and digital wallets, a new technology method for storing cryptocurrencies such as bitcoin and Ethereum. The crypto wallets reside on a user’s computer or mobile device or can reside on a flash drive, as in the case of hardware wallets. The comparison method used is based on the security and integrity of both types of wallets. Additionally, the authors will look at the most important factors that should ensure a high level of social acceptability of the new hardware and digital wallets. The use of digital wallets manifests itself in various ways. The ease of use of being able to complete a transaction in a matter of seconds. The speed with which the transactions are executed reflects the efficiency. This paper discusses the current state of digital wallets on the market, better options for purchasing and using digital wallets, digital wallet security, and future trends in their development, and concludes that both cryptocurrency wallets have advantages and drawbacks. However, hardware wallets are much more secure, but their capabilities are confined. A software wallet is much more convenient to use than a hardware wallet because it can be connected at any time. This novel investigation shows that both wallets are very secure and appropriate for use, as the additional costs of Hardware wallets are minor compared to the costs of paying with actual cash.
This paper looks forward in investigating NFT (Non-Fungible Tokens) Communities through the case study of the CryptoPunks collection as deterritorialized assemblages with a clear ethic posture towards market (financial and art) decentralization, taking into account the interpretations of the meaning(s) of the action, having a view on philosophical theory close to the Crypto community as well as the perception of users and experts on the matter. The investigations works on the direction of finding said meaning through an analysis on the semantic and pragmatic levels, distinguishing between discourses and the practices carried out within the Blockchain, with a particular focus on the Ethereum Network to make emphasis on the mechanisms which allow said assemblage.Additionally, I hope to draw the importance of the human-inhuman interaction in the sociological field, focusing on the possibilities of sociability which are established semantically as well as pragmatically in the technical and technological advancements, taking into account our current time of growing automation and algorithmization of interactions.As a conclusion, we find that ethicity is distributed differentially between the semantic and pragmatic moments, as foundational discourses and the uses taken by the collectionists are far off in their meaning. Such discourses usually go in favour of decentralization of the economy in the privilege of heteronomic and heterarchical forms of constructing value and subjectivities, meanwhile, the uses taken by the collectionists usually undergo in the meaning of maximizing investments which were pushed onto the digital assets tied to NFT contracts, and as such, the community is bonded as far as the speculation of a collection maintains its upward momentum. The community, gestated as an assemblage on its motivation for speed, deterritorialization and decoding is captured by users co-opted by the discourses of capital as an Entrepreneur of himself, seeing digital art assets as maximizers of capital, locking into a loop much like stock market practices.
Çalışmanın amacı, 03.01.2020 ile 28.02.2022 dönemi için üretim araçlarındaki gelişmenin bir başka veçhesi olan dijitalleşme ile kripto paralara yönelimin hızlanmasının geleneksel borsalara alternatif olup olmayacağını simetrik ve asimetrik nedensellik test yöntemleriyle incelemektir. Bu çerçevede simetrik nedensellik analiz sonuçlarına göre, BTC ve ETH fiyatlarından SP500, NASDAQ ve DOWJ fiyatlarına doğru bir nedensellik ilişkisi saptanmış, aynı zamanda VIX’ten BTC ve ETH’ye doğru bir nedensellik ilişkisi bulunmuştur. Asimetrik nedensellik analizi sonuçlarına göre SP500, NASDAQ, DOWJ ve Altın fiyatlarındaki negatif değişmelerden, BTC fiyatlarındaki pozitif değişmelere doğru bir nedensellik ilişkisi tespit edilmişken, NASDAQ ve DOWJ fiyatlarındaki pozitif değişmelerden ETH fiyatlarının pozitif değişmelerine doğru bir nedensellik ilişkisinin varlığına ulaşılmıştır. Son olarak kripto paralar arasındaki nedensellik ilişkisi sınandığında BTC fiyatlarındaki negatif değişimlerden ETH fiyatlarındaki pozitif değişimlere, ETH fiyatlarındaki negatif değişimlerden BTC fiyatlarındaki pozitif değişimlere doğru bir nedensellik ilişkisi tespit edilmiştir.
Blockchain smart contracts can support the decentralisation of business processes, but due to smart contracts’ specifics, their development is a complicated process. Introducing model-driven development principles in smart contract development can facilitate requirement specification, design, and implementation activities. This paper presents a model-driven development method MDAsmartCD (Model-Driven Architecture-based Smart Contract Development) to alleviate smart contract development by supporting the complete MDA life cycle, covering the definition of Computation-Independent Model, Platform-Independent Model, and two instances of Platform-Specific Models. In MDAsmartCD, model transformations (model-to-model and model-to-text) are used to produce smart contract code in the Hyperledger Fabric platform Go and the Ethereum platform Solidity programming languages. The method application was demonstrated by implementing the smart contract for the hackathon solution and executing the generated Solidity and Go smart contracts in the workflow of issuing certificates for hackathon participants. During the execution of the workflow, both deployed smart contracts behaved identically and recorded analogous results in respective blockchain data storages. This demonstrated that the MDAsmartCD method enables the generation of compilable and executable smart contract code, ready for deployment on a blockchain platform.
Blockchain technology has created a new cryptocurrency world and attracted a lot of attention. It also attracts scams, for example, phishing scam, a typical fraud, has been found making a notable amount of money in the blockchain ecosystem, which has a very negative impact. Considering the whole life cycle of a phishing scam, this paper proposes the concept of a phishing gang, that is, a set of accounts that serve for phishing activity and belong to the same entity on the blockchain. As phishers often use multiple accounts to commit phishing scams and money laundering, detecting phishing gangs in the blockchain ecosystem is a real and critical problem. To help deal with this issue, this paper proposes a method of detecting phishing gangs on the Ethereum blockchain. Specifically, we first construct a transaction network with a graph structure by mining the transaction record and the account labels of the Ethereum blockchain. Next, we propose the base and improvement methods of taint analysis, aiming to evaluate the taint score of each account by tracking the fund flow of phishing accounts. Then, with the results of taint analysis and some heuristic means, all accounts in the transaction network are divided into five categories. Based on this, we propose a heuristics algorithm for phishing gang detection. And we also summarize gang patterns and reveal money laundering in phishing activities. Experimental results indicate that the proposed framework can be used to build a uniform platform to monitor every account on the Ethereum blockchain for early warning of phishing scams and detection of the phishers' money laundering and cashing process.
Since it was first founded by Satoshi Nakamoto in 2008, cryptocurrencies have attracted the attention of investors significantly. Until now, many investors have invested their money in cryptocurrencies. This study aims to prove and analyze the effect of volatility, liquidity and world oil price indices on cryptocurrency returns. It also examines whether the occurrence of market efficiency is weak or not. This study aims to prove and analyze weak efficiency market and the impact of liquidity, volatility and the world oil price index on the return of the cryptocurrency market. This study uses quantitative methods and secondary data, so that the number of samples taken are 3 types of cryptocurrencies (Bitcoin, Ethereum and Binance Coin) which are listed on coinmarketcap.com and investing.com, as well as the oil price index taken from investing.com The analytical methods used in this study are series correlation test and runs test as well as multiple linear regression analysis. The results of this study indicate that volatility and liquidity affect the efficiency of the cryptocurrency market while the oil price index has no effect. The results of autocorrelation test on return itself prove that these coins have weak form efficiency. Keywords: Market Efficiency, Cryptocurrency, Liquidity, Oil Price Index, Volatility
Bu çalışmada, kripto piyasasının önde gelen altı kripto para biriminin (Bitcoin, Stellar, Litecoin, Ethereum, Tether ve Ripple) volatil yapısı, asimetrik ilişki ve/ve ya kaldıraç etkisinin var olup olmadığı test edilmektedir. 09/11/2017-31/07/2022 dönemini kapsayan ve WinBUGS uygulaması ile yapılan bu çalışmada öncelikle logaritmik fark alınarak getiri serisi hesaplanmıştır. Bu kapsamda 100.000 tekrarla örneklem sınaması yapılmış olup katsayıların başlangıç eğiliminden çıkması için tahminlerin ilk 10.000 örneklemi dışlanarak kalan 90.000 örneklemle analiz gerçekleştirilmiştir. Asimetrik stokastik volatilite modeli tahmin sonuçlarına göre kripto para birimlerinin oynaklık kalıcılığı, oynaklığın öngörülebilirliği ve para birimlerinin kendi getirilerinin şoku ile oynaklıklarının etkisi arasındaki korelasyon düzeyi ilgili parametreler ile değerlendirilmiştir. Belirtilen zaman aralığında çalışmamızda kullanılan tüm kripto para birimleri için yoğun bir volatilite kümelenmesi olduğu gözlemlenmiştir. Bu volatilitenin sürekli olduğu ve düşük öngörülebilirliğin varlığı ampirik olarak asimetrik stokastik volatilite modeli ile elde edilen bulgular arasındadır. Ayrıca çalışmanın sonuçlarına göre Ethereum kripto para birimi dışındaki diğer beş para biriminin hiçbirinde ne kaldıraç etkisi ne de asimetrik ilişkisinin hiçbiri gözlemlenmemiştir.
The medical technology is being evolved for enhancing the healthcare information related to medication, diseases, medical images etc., by using Electronic Healthcare Recording (EHR). The details such as gender, age, and weight of patients are sensitive, and require protection against unauthorized access. Therefore, providing a robust security for medical data is indeed a challenge. The present research work proposed a Fully Decentralized Block chain (FDBC) model with the Proxy Re-Encryption Algorithm (PReA) with an Ethereum smart contract. The proposed FDBC-PReA allows the data to be visible only for the owner and the smart contract as the model is implemented in an Ethereum based test bed. Each time when the data is transformed, the data loss is obtained for the incorrect data which is taken care by the decentralized BC. The fully decentralized BC stores and checks whether each of the entity shares or accesses the real time data. The proposed FDBC-PReA Ethereum smart contracts, and proxy re-encryption algorithms are executed with a faster rate that consumed average block time of 5.48s, which is lesser when compared to average block times of Smart-Contract Ethereum Distributed Ledger-14s, Searchable