Abstract Investing in cryptocurrencies is progressively becoming a norm; however, these assets are excessively volatile and often decrease or increase in value instantly. Thus, rational investors holding cryptocurrencies for extended periods firmly search for assets that can diversify their risk, preferably with assets other than cryptocurrencies. In this study, we consider the two most studied cryptocurrencies with the highest capitalization and trading volume/value, namely Bitcoin and Ethereum. Specifically, we examine whether high-performing leading US tech stocks (Facebook, Amazon, Apple, Netflix, Google [FAANG]) can provide any diversification benefits to cryptocurrency investors. To do so, we employ dynamic conditional correlation (DCC), asymmetric DCC, time-varying parameter vector autoregression-based connectedness measures, dynamic correlation-based hedge and safe-haven regression analyses, portfolio optimization and hedging strategies, time- and frequency-based wavelet coherence, and high-frequency 10-min intraday data from January 1, 2018 to January 31, 2023. We find that FAANG stocks can be considered (at least weak) safe havens for Bitcoin and Ethereum during the sample period. Our subperiod analyses reveal that the safe-haven role of FAANG stocks, specifically for Bitcoin, has noticeably increased. While the safe-haven property of Facebook is the most promising, for Netflix it is blurred between a weak–safe-haven and a hedge. Our findings may help investors, policymakers, and academicians to invest in cryptocurrencies, formulate relevant investment guidelines, and extend the literature on cryptocurrencies, respectively.
Igor Konnov, Jure Kukovec, Thomas Pani, Roberto Saltini · 5 authors
We investigate automated model-checking of the Ethereum specification, focusing on the Accountable Safety property of the 3SF consensus protocol. We select 3SF due to its relevance and the unique challenges it poses for formal verification. Our primary tools are TLA+ for specification and the Apalache model checker for verification. Our formalization builds on the executable Python specification of 3SF. To begin, we manually translate this specification into TLA+, revealing significant combinatorial complexity in the definition of Accountable Safety. To address these challenges, we introduce several layers of manual abstraction: (1) replacing recursion with folds, (2) substituting abstract graphs with integers, and (3) decomposing chain configurations. To cross-validate our results, we develop alternative encodings in SMT (CVC5) and Alloy. Despite the inherent complexity, our results demonstrate that exhaustive verification of Accountable Safety is feasible for small instances - supporting up to 7 checkpoints and 24 validator votes. Moreover, no violations of Accountable Safety are observed, even in slightly larger configurations. Beyond these findings, our study highlights the importance of manual abstraction and domain expertise in enhancing model-checking efficiency and showcases the flexibility of TLA+ for managing intricate specifications.
Este estudio examina la relación entre las fluctuaciones de precios de la tarjeta gráfica RX 6700XT y la criptomoneda Ethereum (ETH) durante un período de alta volatilidad en el mercado, desde abril de 2021 hasta abril de 2022. Basándose en datos de precios de Estados Unidos, Alemania y Japón, el análisis se centra en la RX 6700XT debido a su popularidad entre los mineros de criptomonedas por su eficiencia energética y su alto hash rate, una medida de rendimiento computacional. Ethereum, seleccionada como la criptomoneda más rentable para la minería de datos con tarjetas gráficas (GPU) durante el período analizado, sirve como base para el análisis metodológico. La metodología se basa en un análisis de series de tiempo, pruebas de estacionariedad, cointegración y la estimación de un modelo de corrección de errores basado en el marco teórico de Gujarati y Porter (2010). Los resultados presentan evidencia empírica de que existe una relación entre los precios de criptomonedas y las tarjetas gráficas, señalando la interacción económica entre estos mercados.
Mathias Hall-Andersen, Mark Simkin, Benedikt Wagner
Towards building more scalable blockchains, an approach known as data availability sampling (DAS) has emerged over the past few years. Even large blockchains like Ethereum are planning to eventually deploy DAS to improve their scalability. In a nutshell, DAS allows the participants of a network to ensure the full availability of some data without any one participant downloading it entirely. Despite the significant practical interest that DAS has received, there are currently no formal definitions for this primitive, no security notions, and no security proofs for any candidate constructions. For a cryptographic primitive that may end up being widely deployed in large real-world systems, this is a rather unsatisfactory state of affairs. In this work, we initiate a cryptographic study of data availability sampling. To this end, we define data availability sampling precisely as a clean cryptographic primitive. Then, we show how data availability sampling relates to erasure codes. We do so by defining a new type of commitment schemes which naturally generalizes vector commitments and polynomial commitments. Using our framework, we analyze existing constructions and prove them secure. In addition, we give new constructions which are based on weaker assumptions, computationally more efficient, and do not rely on a trusted setup, at the cost of slightly larger communication complexity. Finally, we evaluate the trade-offs of the different constructions.
The cryptocurrency market, known for its high volatility and immense data availability, provides an excellent opportunity for predictive modeling. This paper explores the prediction of Ethereum’s price using four distinct models: Random Forest, Logistic Regression, Long Short-Term Memory Networks (LSTM), and CNN-LSTM hybrid models. The study evaluates the performance of these models based on metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Rsquared ( R2 ), and Accuracy (%). The findings highlight that Logistic Regression outperformed the other models with the lowest MSE (6741.12) and highest accuracy (98.66% ) [Table 1]. This research demonstrates the potential of combining traditional and advanced machine learning techniques to achieve robust price prediction in the cryptocurrency domain.
In a world where financial landscapes are constantly evolving, understanding the intricacies of finance becomes not just an asset but a necessity. This book, encompassing a wide range of financial topics, is crafted to demystify the complex world of finance for learners, enthusiasts, and professionals alike. Each lesson in this book is designed as a standalone module, allowing readers to dive into topics at their own pace and according to their interests. Starting with the "Fundamentals of Financial Planning," we lay the groundwork for sound financial understanding. As we progress, topics like "Digital Banking and Online Finance" and "The Role of Technology in Modern Finance" explore the digital revolution reshaping the financial sector. We delve into specific markets with lessons on "Stock Market Indices," "Real Estate Investment Basics," and "Commodities Trading," offering insights into different investment avenues. The book also addresses critical issues such as "Financial Consumer Protection" and "Cybersecurity in Financial Transactions," emphasizing the importance of security in the digital age. Global financial mechanisms are unraveled in lessons like "Understanding the International Monetary Fund (IMF)" and "Global Trade Financing," providing a macroeconomic perspective essential for grasping international finance. The chapters on "The Rise of Cryptocurrencies" and "History of Ethereum" bring readers to the forefront of modern financial innovation, while "Understanding the OneCoin Scam and Scandal" and "Understanding Pyramid Schemes in Finance" serve as cautionary tales highlighting the importance of vigilance in investments. For entrepreneurs, "Financial Literacy for Entrepreneurs" and "Starting and Growing Your Business: Financial Strategies for Entrepreneurs" offer practical guidance on managing and growing a business effectively. We also explore economic systems and government policies in chapters like "Understanding the Communist Economic System" and "Understanding the Cuban Government and Economic System." As we approach the culmination of this journey, "Jobs That May Disappear Due to AI in the Future" and "Understanding the Possibility of a Global Financial Collapse" prompt readers to think about the future of finance and its challenges. This book is more than just a collection of financial lessons; it's a guide to navigating the ever-changing currents of the financial world. Whether you're a student, a professional, or simply curious about finance, these pages are designed to enlighten and empower you in your financial journey.
In smart applications, streaming IoT data is essential to building trust in sustainable IoT solutions. However, most existing systems for storing and disseminating IoT data streams lack reliability, security, and transparency, primarily due to centralized architectures that create single points of failure. To address these limitations, this research introduces TraVel, a blockchain and transfer learning-based framework for secure IoT data management. TraVel leverages decentralized IPFS storage to handle large data volumes effectively, integrating with a private Ethereum blockchain to enhance data integrity and accessibility. In the proposed scheme, the smart home ([Formula: see text]) data is collected securely and accessed over the BC with a unique hash key generated on the IPFS for all the files. Self-executing Ethereum smart contracts enforce access control and verify data integrity, allowing only validated, non-malicious data to be stored. An adversarial domain adaptation (DA) learning model is employed to detect and filter malicious data before it enters the blockchain. TraVel's performance is evaluated on blockchain parameters, with simulations conducted on REMIX IDE and InterPlanetary File System (IPFS), demonstrating its reliability and scalability for secure IoT data dissemination.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Digital currencies like Bitcoin and Ethereum are reshaping the financial landscape by challenging traditional banking and commerce. These technologies hold significant potential to enhance financial inclusion by providing access to underserved populations and streamlining cross-border payments, making transactions faster and more cost-effective. They also empower decentralized finance (DeFi), enabling innovative financial services without intermediaries. Blockchain, the foundation of digital currencies, facilitates secure, decentralized, and programmable systems, supporting key innovations like asset tokenization and Central Bank Digital Currencies (CBDCs). These advancements bridge the gap between digital and traditional financial systems, promoting efficiency and accessibility. Despite their promise, digital currencies face challenges, including cybersecurity threats, market volatility, and fragmented regulatory frameworks. Case studies on CBDCs and private-sector blockchain initiatives demonstrate viable integration pathways and underline the need for robust governance. By implementing balanced policies, stakeholders can harness the benefits of digital currencies while mitigating associated risks. These measures will be crucial to fostering a stable, inclusive, and innovative financial ecosystem that addresses global economic disparities and drives sustainable growth.
Global supply chains, especially in commodity trading, are plagued by fragmentation, lack of transparency, and trust deficits among participants. These issues lead to inefficiencies, increased costs, and an over-reliance on intermediaries. The present Communication describes a blockchain-based platform that leverages Self-Sovereign Identity (SSI) and Verifiable Credentials (VCs) to address these challenges in supply chain management. Developed in collaboration with coffee industry stakeholders, our approach proposes a platform with an integrated marketplace for seller discovery, enables precise order definition with detailed terms and conditions, and actively guides both buyers and sellers throughout the shipping process, managing financial guarantees and ensuring a secure transaction flow. The platform is compatible with both traditional banking infrastructure and modern crypto-based systems, enabling seamless financial transactions. In cases where disputes arise, we empower users to easily collect all communications and documents to present to legal authorities, expediting the resolution process. The platform is implemented using the Internet Computer Protocol (ICP) for secure, on-chain storage and application hosting, and is integrated with the Ethereum blockchain to leverage its extensive decentralized finance (DeFi) ecosystem, significant liquidity, and robust stablecoin infrastructure, thereby facilitating secure financial transactions. Moreover, we introduce an SSI-based authentication and authorization framework that spans across the entire platform, including both the Ethereum Virtual Machine (EVM) and Internet Computer Protocol (ICP), enabling unified role-based access control through verifiable credentials. A value-added of the present Communication, the framework is demonstrated by means of a detailed case study in the coffee industry, highlighting the technical challenges addressed during implementation. While quantitative efficiency metrics will be established through upcoming real-world testing with industry partners, the platform’s design aims to streamline operations by reducing intermediary dependencies and automating key processes. Finally, the Communication provides insights into its adaptability to other industries facing comparable supply chain challenges, presenting an approach focused on enhancing trust and reducing reliance on intermediaries.
(1) Background: Cryptocurrencies have a substantial environmental impact. In particular, the mining procedure that is employed to produce and finalize the transaction is energy-intensive and generates carbon emissions. Consequently, the objective of the present investigation is to investigate the function of cryptocurrencies in a sustainable development. This research specifically investigates the function of stablecoins, a novel subject in finance and academia that has the potential to foster a sustainable business environment. (2) Methods: A bibliometric analysis was performed using the R statistical programming language together with the bibliometric tools Biblioshiny and VOSviewer to fulfill the research objective. Data were obtained from the Scopus database, and their selection was completed using the PRISMA methodology. (3) Results: The results of the current research highlight the crucial role of stablecoins in promoting an alternative decentralized financial sector, offering a unique opportunity for the market to create a more inclusive and environmentally friendly financial ecosystem. Moreover, research indicates that stablecoins might convert Ethereum into a stable currency and enhance their ecologically friendly path. (4) Conclusions: Stablecoins have become a crucial tool in the unpredictable bitcoin environment, offering stability in a tumultuous market. The research indicates that users need to acknowledge the sustainability of asset collateral, and so far, only the regulation of stablecoins is progressing in this area.
Isaac Zhang, Ryan Zarick, Daniel Wong, Thomas Kim · 7 authors
Quick Merkle Database (QMDB) addresses longstanding bottlenecks in blockchain state management by integrating key-value (KV) and Merkle tree storage into a single unified architecture. QMDB delivers a significant throughput improvement over existing architectures, achieving up to 6X over the widely used RocksDB and 8X over NOMT, a leading verifiable database. Its novel append-only twig-based design enables one SSD read per state access, O(1) IOs for updates, and in-memory Merkleization on a memory footprint as small as 2.3 bytes per entry, enabling it to run on even modest consumer-grade PCs. QMDB scales seamlessly across both commodity and enterprise hardware, achieving up to 2.28 million state updates per second. This performance enables support for 1 million token transfers per second (TPS), marking QMDB as the first solution achieving such a milestone. QMDB has been benchmarked with workloads exceeding 15 billion entries (10X Ethereum's 2024 state) and has proven the capacity to scale to 280 billion entries on a single server. Furthermore, QMDB introduces historical proofs, unlocking the ability to query its blockchain's historical state at the latest block. QMDB not only meets the demands of current blockchains but also provides a robust foundation for building scalable, efficient, and verifiable decentralized applications across diverse use cases.
Muhammad Shahzad Ijaz, Shoaib Ali, Anna Min Du, Mahrukh Khurram
We use event study methodology to examine how the Palestine-Israel Conflict affected equities, metals, energy, fiat, and crypto currencies. The findings highlight the susceptibility of the stock markets in Germany, the United Arab Emirates, Bahrain, and Kuwait to geopolitical shocks by demonstrating notable negative abnormal returns on the event day. This observation is more evident in areas which have direct economic connections to the belligerent nations. Conversely, the fiat and cryptocurrency markets, along with metals and oil, exhibit insignificant abnormal returns, with the exception of a strong reaction observed in Ethereum and oil prices. These findings highlight the fluctuating levels of sensitivity across diverse asset classes as markets beyond Palestine's trading partners demonstrate resilience to the war. Overall, our work underscores the significance of assessing contagion risk especially in areas affected by geopolitical instability. It also holds implications for policymakers and investors to contemplate the geopolitical situation while evaluating market risks and portfolio diversification strategies amid political tensions.
Kamilla Nazirkhanova, Vrushank Gunjur, X. Pilli Cruz-De Jesus, Dan Boneh
Ensuring the privacy of votes in an election is crucial for the integrity of a democratic process. Often, voting power is delegated to representatives (e.g., in congress) who subsequently vote on behalf of voters on specific issues. This delegation model is also widely used in Decentralized Autonomous Organizations (DAOs). Although several existing voting systems used in DAOs support private voting, they only offer public delegation. In this paper, we introduce Kite, a new protocol that enables $\textit{private}$ delegation of voting power for DAO members. Voters can freely delegate, revoke, and re-delegate their power without revealing any information about who they delegated to. Even the delegate does not learn who delegated to them. The only information that is recorded publicly is that the voter delegated or re-delegated their vote to someone. Kite accommodates both public and private voting for the delegates themselves. We analyze the security of our protocol within the Universal Composability (UC) framework. We implement Kite as an extension to the existing Governor Bravo smart contract on the Ethereum blockchain, that is widely used for DAO governance. Furthermore, we provide an evaluation of our implementation that demonstrates the practicality of the protocol. The most expensive operation is delegation due to the required zero-knowledge proofs. On a consumer-grade laptop, delegation takes between 7 and 167 seconds depending on the requested level of privacy.
Khalid Khan, Adnan Khurshid, Javier Cifuentes‐Faura
Abstract This study uses the Bayesian structural model to assess the causal effect of the futures exchange (FTX) insolvency on cryptocurrencies from October 2022 to December 14, 2022. Findings show that FTX insolvency negatively impacts cryptocurrencies. Moreover, the results indicate rapid divergence from counterfactual predictions, and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse. Cryptocurrency is reacting strongly to the uncertainty caused by insolvency. In relative terms, the collapse of FTX has been highly detrimental to Solana and Ethereum. Furthermore, the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred. FTX collapsed owing to a mismatch between the assets and liabilities. The industry is still mostly unregulated, and regulators must act quickly, highlighting the need for outstanding innovation and decentralized and trustless technology adoption.
The problem of a single point of failure in centralized systems poses a great challenge to the stability of such systems. Meanwhile, the tamperability of data within centralized systems makes users reluctant to trust and use centralized applications in many scenarios, including the financial and business sectors. Blockchain, as a new decentralized technology, addresses these issues effectively. As a typical decentralized system, blockchain can be utilized to build a data-sharing model. Users in a blockchain do not need to trust other users; instead, they trust that the majority of miner nodes are honest. Smart contracts enable developers to write distributed programs based on blockchain systems, ensuring that all code is immutable and secure. In this paper, we analyze the security of blockchain technology to illustrate its advantages and justify its use. Furthermore, we design a new system for storing and trading vehicle information based on the Ethereum blockchain and smart contract technology. Specifically, our system allows users to upload vehicle information and auction vehicles to transfer ownership. Our application provides great convenience to buyers and owners, while the use of smart contracts enhances the security and privacy of the system.
Danilo Rafael de Lima Cabral, Pedro Antonino, Augusto Sampaio
The Ethereum blockchain has a \emph{gas system} that associates operations with a cost in gas units. Two central concepts of this system are the \emph{gas limit} assigned by the issuer of a transaction and the \emph{gas used} by a transaction. The former is a budget that must not be exhausted before the completion of the transaction execution; otherwise, the execution fails. Therefore, it seems rather essential to determine the \emph{minimum gas limit} that ensures the execution of a transaction will not abort due to the lack of gas. Despite its practical relevance, this concept has not been properly addressed. In the literature, gas used and minimum gas limit are conflated. This paper proposes a precise notion of minimum gas limit and how it can differ from gas used by a transaction; this is also demonstrated with a quantitative study on real transactions of the Ethereum blockchain. Another significant contribution is the proposition of a fairly precise estimator for each of the two metrics. Again, the confusion between these concepts has led to the creation of estimators only for the gas used by a transaction. We demonstrate that the minimum gas limit for the state of the Ethereum blockchain (after the block) $t$ can serve as a near-perfect estimation for the execution of the transaction at block $t + Δ$, where $Δ\leq 11$; the same holds for estimating gas used. These precise estimators can be very valuable in helping the users predict the gas budget of transactions and developers in optimising their smart contracts; over and underestimating gas used and minimum gas limit can lead to a number of practical issues. Overall, this paper serves as an important reference for blockchain developers and users as to how the gas system really works.
Chinchu Paulose, Ansiya P Sham, Anu Krishna P M, Athulya Palanadan · 5 authors
Landslides are natural disasters that cause significant damage to infrastructure, ecosystems, and human life. Accurate and timely prediction of landslides is crucial for reducing the impact of these events. This paper explores a novel approach to landslide prediction using Ethereum, a leading blockchain platform. By leveraging the capabilities of Ethereum, we propose a decentralized system that collects, stores, and analyzes environmental data through smart contracts, providing a transparent, tamper-proof, and efficient way to predict landslides. The system integrates IoT sensors, machine learning models, and blockchain to ensure data integrity, automate alerts, and enhance decision-making processes for disaster management agencies and affected. Key Words: Landslide prediction, Blockchain, Ethereum, Smart contracts, Decentralized data, Environmental monitoring, IoT, Machine learning.
In an era where digitalization has dominated the financial world, cryptographic methods have become the foundation of secure transactions and data integrity. This report conducts an in-depth analysis of the cryptographic methods used in modern cryptocurrencies, namely Bitcoin and Ethereum, and traditional banking systems. The strengths, limitations and implications regarding security and scalability will be highlighted. Bitcoin, employing the usage of Elliptic Curve Cryptography (ECC) and the Secure Hash Algorithm (SHA-256) offers a robust and decentralized architecture heavily resistant to modern threats such as brute force attacks, as well as future threats that may arise with the rapid development of quantum computing. Ethereum takes the fundamental principles of Bitcoin, and enhances them with innovations like Keccak-256, and Recursive Length Prefix (RLP) encoding, optimizing the security and efficiency for complex operations such as smart contracts. Comparatively, traditional banking systems utilize a hybridized cryptographic system, incorporating the usage of methods like AES and ECC to balance security with performance within a centralized financial system, however often constrained by the vulnerabilities methods like AES brings, such as information leakage and overall human error. This comparative analysis highlights the trade-offs between these three systems, offering critical insights into the rapidly evolving role that cryptography is taking in shaping the future of the financial world. The findings presented in this report offer actionable recommendations for advancing cryptographic techniques and adopting decentralized systems to enhance the resilience of commonly used financial systems out in the world today.
Walid Mensi, Ramzi Nekhili, Xuan Vinh Vo, Sang Hoon Kang
ABSTRACT This paper examines the hourly downward/upward multifractality and dynamic efficiency of four cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC)— before and during the COVID‐19 pandemic, and during the Russia–Ukraine tension. Using the asymmetric multifractal detrended fluctuation analysis method, the results show significant asymmetric multifractality in all series, which intensifies for BTC only throughout the COVID‐19 crisis and narrows for ETH, XRP, and LTC. Moreover, we show that cryptocurrency markets are more inefficient during the upward (downward) trend and before (during) the COVID‐19 crisis. LTC is the least inefficient market pre COVID‐19, whereas XRP is the least inefficient during the pandemic crisis. The results show evidence of excessive asymmetric multifractality for all four crypto markets. Before the COVID‐19 crisis, positive values of excess asymmetry in multifractality have been identified for BTC and LTC markets, whereas the excess asymmetry values were negative for ETH and XRP markets. BTC and ETH markets showed wider multifractality fluctuations compared to LTC and XRP, indicating a stronger reaction to the war's impact.
The Bitcoin white paper introduced blockchain technology, enabling trustful transactions without intermediaries. Smart contracts emerged with Ethereum and blockchains expanded beyond cryptocurrency, applying to auctions, crowdfunding and electronic voting. However, blockchain's transparency raised privacy concerns and initial anonymity measures proved ineffective. Smart contract privacy solutions employed zero-knowledge proofs, homomorphic encryption and trusted execution environments. These approaches have practical drawbacks, such as limited functionality, high computation times and trust on third parties requirements, being not fully decentralized. This work proposes a solution utilizing zk-SNARKs to provide privacy in smart contracts and blockchains. The solution supports both fungible and nonfungible tokens. Additionally, the proposal includes a new type of transactions, called delegated transactions, which enable use cases like Delivery vs Payment (DvP).
Smart contracts enable autonomous execution between contracting parties without a centralized authority, thereby reducing contract management costs and enhancing the transparency and reliability of contracts. However, the absence of such a certification authority increases the risk of fraud. Rug-pull, a typical form of fraud, involves developers hiding backdoor codes in smart contracts to steal funds under certain conditions, causing significant damage to users. A Rug-pull list warns users of potential fraud, but it only identifies risks after damage has occurred. Additionally, existing backdoor code analysis tools are limited in their ability to detect backdoor codes hidden through modifications to existing patterns or suffer from low accuracy because they rely on comparisons with predefined backdoor codes. Therefore, this paper proposes a balance-tracking-based backdoor code detection model to identify backdoor codes in smart contracts. The proposed model detects backdoor codes by extracting functions from Ethereum bytecodes and inspecting the extracted functions to track balance changes. This approach allows for the detection of balance changes even when backdoor codes are concealed. Experimental results verifying the effectiveness of this model demonstrate 98% accuracy, 0.96 recall, and 0.98 precision. These results are expected to contribute significantly to effectively reducing fraud risks such as Rug-pull.
Public blockchain has outstanding performance in transaction privacy protection because of its anonymity. The data openness brings feasibility to transaction behavior analysis. At present, the transaction data of the public chain are huge, including complex trading objects and relationships. It is difficult to extract attributes and predict transaction behavior by traditional methods. To solve the problems, we extract the transaction features to construct the Ethereum transaction heterogeneous information network (HIN), and propose graph-neural-network-based transaction prediction method for public blockchain in HINs, which can divide the network into subgraphs according to connectivity and make the prediction results of transaction behavior more accurate. Experiments show that the execution time consumption of the proposed transaction subgraph division method is reduced by 70.61% on average compared with the search method. The accuracy of the proposed behavior prediction method also improve compared with the traditional random walk method, with an average accuracy of 83.82%.
This paper presents a comprehensive exploration of the intersection between machine learning and smart contract vulnerabilities on the Ethereum blockchain. Introduced by Vitalik Buterin in 2015, Ethereum stands as a prominent blockchain network, necessitating innovative approaches to secure smart contracts against vulnerabilities and potential attacks. This research follows PRISMA guidelines, posing three fundamental questions and conducting a meticulous literature review. The study categorises machine learning applications into seven distinct groups, analysing their taxonomy, feature types, and engineering methods. The findings indicate a dynamic landscape characterised by a noticeable trend towards increased complexity. This complexity is evident not only in the integration of machine learning frameworks that combine different architectures of deep learning models, such as Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), or Recurrent Neural Networks (RNN), but also in the incorporation of various types of data related to smart contracts (SCs). The discussion dissects the advantages, limitations, and future directions in securing smart contracts using machine learning. The paper concludes by emphasising the evolving role of machine learning in strengthening the Ethereum blockchain, fostering trust, and enhancing security in decentralised systems.
Cryptocurrencies have introduced a transformative paradigm in financial technology, challenging traditional financial structures and creating novel transactional frameworks. With the rapid expansion of the cryptocurrency market, the need for objective assessment and comparative analysis of leading digital assets has become increasingly pertinent. This study presents a detailed, data-driven evaluation of five prominent cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Tether (USDT), USD Coin, and Lido Staked Ether (STETH). Drawing on an extensive dataset sourced from IntoTheBlock, a leading platform for cryptocurrency analytics, we assess these cryptocurrencies based on selected efficiency indicators. Our research methodology encompasses a systematic exploration of financial and network metrics, including market capitalization, volatility, daily active addresses, and transaction statistics. The results provide nuanced insights into the relative performance of these assets, identifying Bitcoin as the most efficient based on the selected criteria. This work emphasizes the significance of empirical, data-centric methodologies, eschewing subjective judgments, to deliver actionable insights for investors, policymakers, and scholars in the domain of decentralized finance.