This article examines the innovative integration of blockchain technology within the educational domain, as illustrated through the comprehensive development of the K12Net ecosystem. Central to this exploration is the implementation of a blockchain framework to revolutionize the management of educational assets, smart contracts, and the facilitation of a novel educational rewards system. Through the strategic deployment of blockchain's immutable, transparent, and secure attributes, the K12Net initiative endeavors to enhance educational administration, streamline student assessments, curriculum management, and foster a secure environment for the exchange of educational resources. At the heart of the K12Net project is the establishment of a decentralized ecosystem, where educational assets are digitized and overseen through smart contracts, heralding unprecedented efficiency, transparency, and security in educational transactions and data management. This endeavor not only optimizes administrative operations but also paves the way for secure and verifiable academic credentialing, thus nurturing trust among students, educators, and educational institutions alike. Furthermore, this article delves into the intricate technical architecture of the K12Net blockchain system, underscoring its utilization of the Ethereum blockchain networks, the intricacies of smart contract functionalities, and the seamless integration with pre-existing educational services. A comprehensive analysis of the project's aims, developmental methodologies, and potential impacts sheds light on both the benefits and challenges associated with employing blockchain technology in the educational sphere.
Mehrzad Mohammadi, Reza Javan, Mohammad Beheshti-Atashgah, Mohammad Reza Aref
Internet of Things (IoT) devices are capable of allowing for far-reaching access to and evaluation of patient data to monitor health and diagnose from a distance. An electronic healthcare system that checks patient data, prepares medicines and provides financial assistance is necessary. Providing safe data transmission, monitoring, decentralization, preserving patient privacy, and maintaining confidentiality are essential to an electronic healthcare system. In this study, we introduce (SCALHEALTH) which is a blockchain-based scheme of the Hyperledger Fabric consortium. In this study, we use authentication to agree on a common key for data encryption to send data confidentially. Also, sending data through IPFS is decentralized. Non-fungible token (NFT) is used to send patient prescriptions to pharmacies and insurance companies to ensure the authenticity of patient prescriptions. As the system's main body, blockchain creates authorization and validation for all devices and institutions. Also, all metadata in the system is recorded on the blockchain to maintain integrity, transparency, and timely data monitoring. The proposed study uses two types of blockchain: a health blockchain and a financial blockchain. The financial blockchain is for financial transactions and is based on Ethereum. The health blockchain also introduces a mechanism that allows several blockchains to be active in parallel, instead of only one blockchain. The prototype of this mechanism is simulated in two scenarios. In comparison to the normal state, the proposed plan has superior results.
Utilizing graph analytics and learning has proven to be an effective method for exploring aspects of crypto economics such as network effects, decentralization, tokenomics, and fraud detection. However, the majority of existing research predominantly focuses on leading cryptocurrencies, namely Bitcoin (BTC) and Ethereum (ETH), overlooking the vast diversity among the more than 10,000 cryptocurrency projects. This oversight may result in skewed insights. In our paper, we aim to broaden the scope of investigation to encompass the entire spectrum of cryptocurrencies, examining various coins across their entire life cycles. Furthermore, we intend to pioneer advanced methodologies, including graph transfer learning and the innovative concept of "graph of graphs". By extending our research beyond the confines of BTC and ETH, our goal is to enhance the depth of our understanding of crypto economics and to advance the development of more intricate graph-based techniques.
Ethereum has recently switched to a Proof of Stake consensus protocol called Gasper. We analyze Gasper using PRISM+ , an extension of the probabilistic model checker PRISM with primitives for modeling blockchain data types . PRISM+ is therefore used to rapidly and automatically analyze the robustness of Gasper when tuning, up or down, several basic parameters of the protocol, such as network latencies and number of validators. We also study the effectiveness of Gasper in updating stakes and its resilience to three attacks: the balance, bouncing and time attacks.
Abstract Ethereum smart contracts are a special type of computer programs. Once deployed on the blockchain, they cannot be modified. This presents a significant challenge to the security of smart contracts. Previous research has proposed static and dynamic detection tools to identify vulnerabilities in smart contracts. These tools check contract vulnerabilities based on predefined rules, and the accuracy of detection strongly depends on the design of the rules. However, the constant emergence of new vulnerability types and strategies for vulnerability protection leads to numerous false positives and false negatives by tools. To address this problem, we analyze the characteristics of vulnerabilities in smart contracts and the corresponding protection strategies. We convert the contracts' bytecode into an intermediate representation to extract semantic information of the contracts. Based on this semantic information, we establish a set of detection rules based on semantic facts and implement a vulnerability detection tool SafeCheck using static program analysis methods. The tool is used to detect six common types of vulnerabilities in smart contracts. We have extensively evaluated SafeCheck on real Ethereum smart contracts and compared it to other tools. The experimental results show that SafeCheck performs better in smart contract vulnerability detection compared to other typical tools, with a high F‐measure (up to 83.1%) for its entire dataset.
This study investigates the influence of structural change on the diversification, hedging, and safe-haven characteristics of Bitcoin and Ethereum against various financial assets such as gold, the US Dollar Index, stock indices, oil, and commodity indices from August 7, 2015, to August 15, 2022, using the DCC–ARMA–GARCH models with the CUSUM test. Our results indicate that cryptocurrencies have the same characteristics vis-à-vis financial markets during the entire sample period and periods tied to the date of major international events (COVID-19 and the early-2022 Russia–Ukraine War). However, we find that cryptocurrencies play different roles against specific asset markets in different periods separated by structural change models. Our findings suggest that incorporating structural changes into a model accounts for higher volatility and may better describe the real-world capabilities of cryptocurrencies against financial assets.
Hugo Prasetyo Winotoatmojo, Samuel Yesua Lazuardy, Fabian Arland, Antonius Ary Setyawan
The rapid growth of cryptocurrencies over the past 14 years has led to increased deep-level mining activities. This research aims to explore the environmental impacts resulting from the surge in crypto mining and proposed solutions to mitigate these impacts. Cryptocurrencies, gaining popularity as alternative investments and global payment tools, have significantly boosted crypto mining activities. However, the increasing number of transactions requiring computer validation has resulted in adverse consequences for the environment, particularly in terms of substantial energy consumption. Literature review and systematic analysis were conducted to comprehend the environmental impact of crypto mining, focusing on major cryptocurrencies such as Bitcoin, Ethereum, and others. The analysis highlights that crypto mining, especially Bitcoin, requires a significant amount of electricity, leading to a substantial carbon footprint and broad environmental repercussions. Proposed solutions to address the environmental impact of crypto mining include the use of renewable energy sources such as solar and wind power, enhancing the efficiency of specialized mining devices (ASICs), and exploring more energy-efficient consensus mechanisms like Proof of Stake (PoS) compared to the currently utilized Proof of Work (PoW). Reducing redundancy in blockchain technology has also been identified as a crucial step in minimizing unnecessary energy consumption. However, this research has limitations concerning data consistency, a comprehensive understanding of overall environmental impacts, and continuous technological changes in the crypto world. Therefore, future research should focus on developing more efficient consensus mechanisms, effective policy frameworks and governance, as well as real-world implementation studies to evaluate the sustainability solutions proposed.
Flavio Corradini, Alessandro Marcelletti, Andrea Morichetta, Barbara Re
The broader adoption of blockchain for creating decentralised applications has raised interest in employing analysis techniques to support continuous improvement. Data extraction is crucial in this context, as it permits a better understanding of how applications behave. However, due to the variety of data sources (e.g., transactions and events) and the characterisation of the blockchain structure, several challenges arise in automatically extracting data. In particular, retrieving smart contract state changes remains unexplored despite its potential usage for discovering unexpected behaviour. For such reasons, this work proposes a methodology and a supporting tool for extracting data from smart contract executions and state changes. The obtained data is then offered in a way that can be easily converted to purpose-specific standards. The methodology was tested on the PancakeSwap Ethereum bridge smart contract.
This article provides a statistical approach to describe the fit of the most popular cryptocurrencies, building off a previous report, "A Statistical Analysis of Cryptocurrencies." We examined Bitcoin, Ethereum, Tether, Binance, Ripple, Cardano, Solana, and Doge coins. To model our cryptocurrencies, we utilized trading prices between 2017 and 2022 in light of historic events, such as the COVID-19 pandemic. Additionally, we performed a correlation analysis to help understand the relationship between the popular cryptos. Here, we report that the candidate distributions we fit to model the currencies needed to be more independent to describe the return of all popular cryptos. This could be due to the need for Correlation between some of these popular cryptos. We found the generalized hyperbolic and the generalized t showed the best performance of the models tested, though these approaches remained limited in their overall fitness. Their performance also varied by cryptocurrency under investigation, with Tether demonstrating the worst fit across all candidate models. Using our fit models, we also predicted the average daily returns for January 1st, 2023, to February 1st, 2023, and generally found good predictive validity. These results are critical in understanding the movements of cryptos and help better understand the risk associated with trading these currencies.
Abstract: Almost a century ago, the philosopher, cultural critic, and essayist Walter Benjamin grappled with the evolution of "The Work of Art in the Age of Mechanical Reproduction." Although art had experienced imperfect imitations and reproductions throughout history, advancements like photography and film in Benjamin's time drastically heightened the efficiency and fidelity of replication. This shift raised profound questions about the notions of "originality" and "authenticity," distancing reproduced works from the unique "aura" of their originals. Fast forward to our present digital age, where a few clicks or lines of code can effortlessly generate flawless replicas, improved duplicates, or even entirely fabricated "deep fakes." However, the advent of immutable blockchain ledgers, pioneered by Bitcoin, Ethereum, and other cryptocurrencies, and harnessed by non-fungible tokens (NFTs), is ushering in a new era of originality. Crucially, this new era encompasses provable originality and authenticity, paired with indisputable ownership and robust programming capabilities. Similar to how Bitcoin resolved the "double spending" predicament in our digital age, NFTs are now initiating a transformative shift in conventional notions of ownership and provenance while introducing novel forms of originality. Despite existing solely in digital form, crafted through programmable code, smart contracts, and technological protocols, an NFT can maintain its distinctive aura and original essence.
Open access
Blockchain Technology Applications and Security
Neuroethics, Human Enhancement, Biomedical Innovations
This study examines the day-of-the-week (DoW) and month-of-the-year (MoY) effects in the cryptocurrency market, with a focus on Bitcoin (BTC) and Ethereum (ETH). Due to the absence of a specific closing time in the cryptocurrency market, the closing time of the daily data is taken as 23:59 UTC. Initially, an appropriate volatility model for the cryptocurrency market is established using the GARCH, EGARCH, and TGARCH models. The most appropriate model for BTC is ARMA(1,0)-EGARCH(1,1) and ARMA(1,0)-GARCH(1,1) for ETH. The results of the analysis indicate a leverage effect in the cryptocurrency market, where negative shocks cause a more significant increase in volatility than positive shocks. Based on this volatility structure, the DoW and MoY are analyzed. For BTC, returns on other days are lower compared to Mondays. However, for ETH, returns on Thursdays are lower than those on Mondays. In terms of volatility, both BTC and ETH show that the highest volatility occurs on Mondays. For the MoY effect, neither BTC nor ETH don’t exhibit a significant effect in the mean equation. Nevertheless, the variance equation indicates that January has higher volatility compared to other months, indicating the presence of a MoY effect in terms of volatility.
Moustafa Mowaffak Saad, Dalia Sobhy, Amani A. Saad
Blockchainsand smart contracts are pivotal in transforming interactions between systems and individuals, offering secure, immutable, and transparent trust-building mechanisms without central oversight. However, Smart Contracts face limitations due to their reliance on blockchain-contained data, a gap addressed by ’Oracles’. These bridges to external data sources introduce the ’Oracle problem’, where maintaining blockchain-like security and transparency becomes vital to prevent data integrity issues. This paper presents Veritas, a novel decentralized oracle system leveraging a layer-2 scaling solution, enhancing smart contracts’ efficiency and security on Ethereum blockchains. The proposed architecture, explored through simulation and experimental analyses, significantly reduces operational costs while maintaining robust security protocols. An innovative node selection process is also introduced to minimize the risk of malicious data entry, thereby reinforcing network security. Veritas offers a solution to the Oracle problem by aligning with blockchain principles of security and transparency, and demonstrates advancements in reducing operational costs and bolstering network integrity. While the study provides a promising direction, it also highlights potential areas for further exploration in blockchain technology and oracle system optimization.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
With the emergence of blockchain technology, the cryptocurrency market has experienced significant growth in recent years, simultaneously fostering environments conducive to cybercrimes such as phishing scams. Phishing scams on blockchain platforms like Ethereum have become a grave economic threat. Consequently, there is a pressing demand for effective detection mechanisms for these phishing activities to establish a secure financial transaction environment. However, existing methods typically utilize only the most recent transaction record when constructing features, resulting in the loss of vast amounts of transaction data and failing to adequately reflect the characteristics of nodes. Addressing this need, this study introduces a multiscale feature fusion approach integrated with a graph convolutional network model to detect phishing scams on Ethereum. A node basic feature set comprising 12 features is initially designed based on the Ethereum transaction dataset in the basic feature module. Subsequently, in the edge embedding representation module, all transaction times and amounts between two nodes are sorted, and a gate recurrent unit (GRU) neural network is employed to capture the temporal features within this transaction sequence, generating a fixed-length edge embedding representation from variable-length input. In the time trading feature module, attention weights are allocated to all embedding representations surrounding a node, aggregating the edge embedding representations and structural relationships into the node. Finally, combining basic and time trading features of the node, graph convolutional networks (GCNs), SAGEConv, and graph attention networks (GATs) are utilized to classify phishing nodes. The performance of these three graph convolution-based deep learning models is validated on a real Ethereum phishing scam dataset, demonstrating commendable efficiency. Among these, SAGEConv achieves an F1-score of 0.958, an AUC-ROC value of 0.956, and an AUC-PR value of 0.949, outperforming existing methods and baseline models.
This research aims to empirically investigate the portfolio risk associated with crypto assets. In other words, we want to investigate whether the inclusion of crypto assets in a portfolio can minimize the portfolio risk or not, because it is argued that there is a lower degree of correlation between crypto assets and traditional assets. In order to achieve our research objectives, we employ the Vector Autoregressive Model (VAR) by using five different asset classes. The first two variables are taken from the crypto assets, Bitcoin and Ethereum, and the remaining three variables for Gold, Crude Oil and VIX (Chicago Board Options Exchange's (CBOE) volatility index). Our research strategy will be based on an analysis for unit root, optimal lag selection, coefficient matrix, checking VAR stability, the Granger causality test, and impulse response function (IRF). Our findings suggest that none of the indicators of traditional assets drive and explain Bitcoin. We also found that only Bitcoin is significantly related to Ethereum. while none of the other variables are statistically useful to explain the variation in the Ethereum. Based on these findings it can be recommended that the inclusion of crypto assets into a portfolio reduces risk because none of the indicators of crypto assets are significantly related to the indicators of traditional assets.
This study presents an innovative approach for predicting cryptocurrency time series, specifically focusing on Bitcoin, Ethereum, and Litecoin. The methodology integrates the use of technical indicators, a Performer neural network, and BiLSTM (Bidirectional Long Short-Term Memory) to capture temporal dynamics and extract significant features from raw cryptocurrency data. The application of technical indicators, such facilitates the extraction of intricate patterns, momentum, volatility, and trends. The Performer neural network, employing Fast Attention Via positive Orthogonal Random features (FAVOR+), has demonstrated superior computational efficiency and scalability compared to the traditional Multi-head attention mechanism in Transformer models. Additionally, the integration of BiLSTM in the feedforward network enhances the model's capacity to capture temporal dynamics in the data, processing it in both forward and backward directions. This is particularly advantageous for time series data where past and future data points can influence the current state. The proposed method has been applied to the hourly and daily timeframes of the major cryptocurrencies and its performance has been benchmarked against other methods documented in the literature. The results underscore the potential of the proposed method to outperform existing models, marking a significant progression in the field of cryptocurrency price prediction.
Carlos Alberto Oliveira Rodrigues, Júlio César Ferreira Rolim, Pedro Henrique Silva Gabi
A tecnologia Blockchain se destaca não somente pela relação direta com ativos digitais, a exemplo das criptomoedas, mas também pelas variadas aplicações (assinaturas digitais, hash criptográfico, algoritmos de consenso distribuído e contratos inteligentes) que impulsionam o desenvolvimento e segurança dos setores financeiro, saúde, governo, manufatura e distribuição. Em 2021 a sigla NFT (Non-Fungible Tokens) ou Tokens Não Fungíveis ganhou destaque internacional como uma tecnologia inovadora que conectou o mundo da arte ao Blockchain, sendo inclusive nomeada como “Word of the Year” pela editora de dicionário Collins. O presente trabalho tem como principal objetivo discutir a viabilidade do NFT como meio para proteção dos direitos autorais de obras digitais e digitalizadas, a partir do levantamento e análise de artigos científicos, notícias e relatórios relacionados à tecnologia NFT no Blockchain Ethereum à luz da Lei nº 9.610, de 19 de fevereiro de 1998, Lei de Direitos Autorais (LDA) brasileira.
This paper analyzes the Execution Tickets proposal on Ethereum Research, unveiling its potential to revolutionize the Ethereum blockchain's economic model. At the core of this proposal lies a novel ticketing mechanism poised to redefine how the Ethereum protocol distributes the value associated with proposing execution payloads. This innovative approach enables the Ethereum protocol to directly broker Maximal Extractable Value (MEV), traditionally an external revenue stream for validators. The implementation of Execution Tickets goes beyond optimizing validator compensation; it also introduces a new Ethereum native asset with a market capitalization expected to correlate closely with the present value of all value associated with future block production. The analysis demonstrates that the Execution Ticket system can facilitate a more equitable distribution of value within the Ethereum ecosystem, and pave the way for a more secure and economically robust blockchain network.
The execution of smart contracts on Ethereum, a public blockchain system, incurs a fee called gas fee for its computation and data storage. When programmers develop smart contracts (e.g., in the Solidity programming language), they could unknowingly write code snippets that unnecessarily cause more gas fees. These issues, or what we call gas wastes, can lead to significant monetary losses for users. This paper takes the initiative in helping Ethereum users reduce their gas fees in two key steps. First, we conduct an empirical study on gas wastes in open-source Solidity programs and Ethereum transaction traces. Second, to validate our study findings, we develop a static tool called PeCatch to effectively detect gas wastes in Solidity programs, and manually examine the Solidity compiler's code to pinpoint implementation errors causing gas wastes. Overall, we make 11 insights and four suggestions, which can foster future tool development and programmer awareness, and fixing our detected bugs can save $0.76 million in gas fees daily.
Abstract: Blockchain is one of the most trending technologies which plays a major role in online transactions using cryptocurrencies. The blockchain is a chain of blocks that consists of allthe transactions up to the size of 1MB. This blockchain has many properties such as decentralization, immutability, transparency, and audibility, making transactions more secure and tamper-proof. It is tamper-proof because there is no possibility to tampera block as every block in the blockchain has the hash of the previous block. And among cryptocurrencies, bitcoin is one ofthe most popular... In fact, blockchain was introduced to theworld because of bitcoins. Apart from cryptocurrency, blockchain technology can be used in financial, NYC, and social services, risk management, food, healthcare facilities, and so on. Numerous studies have examined the potential that blockchain offers inmultiple application sectors, as well as the benefits and different kinds of blockchain. This paper presents a comparative studyof bitcoins in blockchain technology and the workflow of thebitcoin, compares bitcoin and Ethereum, and provides the use of smart contracts in this emerging technology. The methodology considered for this research is the Ethereum blockchain in smart contracts using a solidity programming language.
Abstract This study uses high-frequency (1-min) price data to examine the connectedness among the leading cryptocurrencies (i.e. Bitcoin, Ethereum, Binance, Cardano, Litecoin, and Ripple) at volatility and high-order (third and fourth orders in this paper) moments based on skewness and kurtosis. The sample period is from February 10, 2020, to August 20, 2022, which captures a pandemic, wartime, cryptocurrency market crashes, and the full collapse of a stablecoin. Using a time-varying parameter vector autoregressive (TVP-VAR) connectedness approach, we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data. Moreover, all estimators are time dependent and affected by significant events. As an exception, the Russia–Ukraine War did not increase the total connectedness among cryptocurrencies. Analysis of third- and fourth-order moments reveals additional dynamics not captured by the second moments, highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market. Additional tests show that rolling-window-based VAR models do not reveal these patterns. Regarding the directional risk transmissions, Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network. In contrast, skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks. These findings are expected to serve as a guide for portfolio optimization, risk management, and policy-making practices.
In recent years, Decentralized Finance (DeFi) has grown rapidly due to the development of blockchain technology and smart contracts. As of March 2023, the estimated global cryptocurrency market cap has reached approximately $949 billion. However, security incidents continue to plague the DeFi ecosystem, and one of the most notorious examples is the “Rug Pull” scam. This type of cryptocurrency scam occurs when the developer of a particular token project intentionally abandons the project and disappears with investors’ funds. Despite only emerging in recent years, Rug Pull events have already caused significant financial losses. In this work, we manually collected and analyzed 103 real-world rug pull events, categorizing them based on their scam methods. Two primary categories were identified:Contract-relatedRug Pull (through malicious functions in smart contracts) andTransaction-relatedRug Pull (through cryptocurrency trading without utilizing malicious functions). Based on the analysis of rug pull events, we propose CRPWarner (short forContract-relatedRugPull RiskWarner) to identify malicious functions in smart contracts and issue warnings regarding potential rug pulls. We evaluated CRPWarner on 69 open-source smart contracts related to rug pull events and achieved a 91.8% precision, 85.9% recall, and 88.7% F1-score. Additionally, when evaluating CRPWarner on 13,484 real-world token contracts on Ethereum, it successfully detected 4168 smart contracts with malicious functions, including zero-day examples. The precision of large-scale experiments reaches 84.9%.
N, Jeba, S Anas, S Anuragav, R. Abhishek · 5 authors
Innovative solution for addressing the challenges in the legal records management system through a blockchain-based eVault platform. Our objective is to create a secure, transparent, and accessible ecosystem that caters to the needs of all stakeholders, including lawyers, judges, clients, and registrars. First and foremost, our solution is built on a robust blockchain platform like Ethereum harnessing the power of smart contracts to manage access, permissions, and transactions effectively. This ensures the utmost security and transparency in every interaction within the system. To make our eVault system user-friendly, we've developed intuitive interfaces for all stakeholders. Lawyers, judges, clients, and even registrars can effortlessly upload and retrieve legal documents, track changes, and share information within the platform. But that's not all; we've gone a step further by incorporating a document creation and saving feature within our app and website. This feature allows users to generate and securely store legal documents, streamlining the entire documentation process.
U radu su opisane faze ažuriranja Ethereum 1.0 mreže na Ethereum 2.0 mrežu. Radi demonstracije načina na koji se mogu inspektovati podaci na Ethereum mreži, kao što su izvršene transakcije i kreirani blokovi, korišćeni su istraživači blokova. Za kreiranje novčanika iskorišten je MetaMask, dok je kao istraživač blokova korišten EtherScan. Transakcije su izvršene i pregledane na testnoj mreži Sepolia.
Upgradeable smart contracts (USCs) have been widely adopted to enable modifying deployed smart contracts. While USCs bring great flexibility to developers, improper usage might introduce new security issues, potentially allowing attackers to hijack USCs and their users. In this paper, we conduct a large-scale measurement study to characterize USCs and their security implications in the wild. We summarize six commonly used USC patterns and develop a tool, USCDetector, to identify USCs without needing source code. Particularly, USCDetector collects various information such as bytecode and transaction information to construct upgrade chains for USCs and disclose potentially vulnerable ones. We evaluate USCDetector using verified smart contracts (i.e., with source code) as ground truth and show that USCDetector can achieve high accuracy with a precision of 96.26%. We then use USCDetector to conduct a large-scale study on Ethereum, covering a total of 60,251,064 smart contracts. USCDetecor constructs 10,218 upgrade chains and discloses multiple real-world USCs with potential security issues.